00475nas a2200109   4500008004100000245008100041210006900122100002300191700001900214700002100233856011100254       2023                        eng d00aEvaluating the Privacy Exposure of Interpretable Global and Local Explainers0 aEvaluating the Privacy Exposure of Interpretable Global and Loca1 aNaretto, Francesca1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/evaluating-privacy-exposure-interpretable-global-and-local-explainers00481nas a2200169   4500008004100000245003300041210003300074260001400107100002100121700002300142700001900165700002100184700002200205700002500227700002000252856003900272       2022                        eng d00aMonitoring Fairness in HOLDA0 aMonitoring Fairness in HOLDA  bIOS Press1 aFontana, Michele1 aNaretto, Francesca1 aMonreale, Anna1 aGiannotti, Fosca1 aSchlobach, Stefan1 aPérez-Ortiz, María1 aTielman, Myrthe  uhttps://doi.org/10.3233/FAIA22020500523nas a2200157   4500008004100000245007200041210006900113490001900182100002200201700002100223700002300244700002300267700002000290700001800310856003700328       2021                        eng d00aBenchmarking and Survey of Explanation Methods for Black Box Models0 aBenchmarking and Survey of Explanation Methods for Black Box Mod0 vabs/2102.130761 aBodria, Francesco1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aNaretto, Francesca1 aPedreschi, Dino1 aRinzivillo, S  uhttps://arxiv.org/abs/2102.1307600361nas a2200121   4500008004100000245003500041210003500076260001300111100002100124700002300145700002200168856004900190       2021                        eng d00aExplainable for Trustworthy AI0 aExplainable for Trustworthy AI  bSpringer1 aGiannotti, Fosca1 aNaretto, Francesca1 aBodria, Francesco  uhttps://doi.org/10.1007/978-3-031-24349-3_1003888nas a2200589   4500008004100000020001400041245010600055210006900161260001500230520215400245100001702399700002302416700002802439700002102467700002202488700001802510700002702528700002302555700001702578700001602595700001802611700002102629700002602650700002102676700002102697700002302718700001802741700001702759700002102776700001802797700001702815700001902832700001702851700002902868700001902897700002102916700001802937700002302955700002202978700002003000700001903020700001903039700002303058700001803081700002403099700001703123700001803140700002203158700002603180700002703206856006503233       2021                        eng d  a1572-843900aGive more data, awareness and control to individual citizens, and they will help COVID-19 containment0 aGive more data awareness and control to individual citizens and   c2021/02/023 aThe rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.1 aNanni, Mirco1 aAndrienko, Gennady1 aBarabasi, Albert-Laszlo1 aBoldrini, Chiara1 aBonchi, Francesco1 aCattuto, Ciro1 aChiaromonte, Francesca1 aComandé, Giovanni1 aConti, Marco1 aCoté, Mark1 aDignum, Frank1 aDignum, Virginia1 aDomingo-Ferrer, Josep1 aFerragina, Paolo1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aHelbing, Dirk1 aKaski, Kimmo1 aKertész, János1 aLehmann, Sune1 aLepri, Bruno1 aLukowicz, Paul1 aMatwin, Stan1 aJiménez, David, Megías1 aMonreale, Anna1 aMorik, Katharina1 aOliver, Nuria1 aPassarella, Andrea1 aPasserini, Andrea1 aPedreschi, Dino1 aPentland, Alex1 aPianesi, Fabio1 aPratesi, Francesca1 aRinzivillo, S1 aRuggieri, Salvatore1 aSiebes, Arno1 aTorra, Vicenc1 aTrasarti, Roberto1 avan den Hoven, Jeroen1 aVespignani, Alessandro  uhttps://link.springer.com/article/10.1007/s10676-020-09572-w02525nas a2200205   4500008004100000020001400041245007100055210006900126260001600195300001100211490000800222520189700230100001802127700002302145700001902168700001902187700002002206700002102226856007202247       2021                        eng d  a0004-370200aGLocalX - From Local to Global Explanations of Black Box AI Models0 aGLocalX From Local to Global Explanations of Black Box AI Models  c2021/05/01/  a1034570 v2943 aArtificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLocalX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLocalX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLocalX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLocalX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S000437022100008402309nas a2200205   4500008004100000022001400041245005800055210005800113260001600171300001400187490000700201520153700208100001801745700001401763700001901777700002001796700002101816700001501837856025101852       2020                        eng d  a1545-597100aAuthenticated Outlier Mining for Outsourced Databases0 aAuthenticated Outlier Mining for Outsourced Databases  cJan-03-2020  a222 - 2350 v173 aThe Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records (ARs) into the dataset, from which it constructs a set of artificial outliers (AOs) and artificial non-outliers (ANOs). The AOs and ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify ANOs and AOs without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.1 aDong, Boxiang1 aWang, Hui1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, Wenge  uhttps://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8858https://ieeexplore.ieee.org/document/8048342/http://xplorestaging.ieee.org/ielx7/8858/9034462/08048342.pdf?arnumber=8048342https://ieeexplore.ieee.org/ielam/8858/9034462/8048342-aam.pdf01834nas a2200145   4500008004100000245006100041210006100102260001300163520137000176100001401546700001701560700002101577700002301598856006701621       2020                        eng d00aDigital Footprints of International Migration on Twitter0 aDigital Footprints of International Migration on Twitter  bSpringer3 aStudying migration using traditional data has some limitations. To date, there have been several studies proposing innovative methodologies to measure migration stocks and flows from social big data. Nevertheless, a uniform definition of a migrant is difficult to find as it varies from one work to another depending on the purpose of the study and nature of the dataset used. In this work, a generic methodology is developed to identify migrants within the Twitter population. This describes a migrant as a person who has the current residence different from the nationality. The residence is defined as the location where a user spends most of his/her time in a certain year. The nationality is inferred from linguistic and social connections to a migrant’s country of origin. This methodology is validated first with an internal gold standard dataset and second with two official statistics, and shows strong performance scores and correlation coefficients. Our method has the advantage that it can identify both immigrants and emigrants, regardless of the origin/destination countries. The new methodology can be used to study various aspects of migration, including opinions, integration, attachment, stocks and flows, motivations for migration, etc. Here, we exemplify how trending topics across and throughout different migrant communities can be observed.1 aKim, Jisu1 aSirbu, Alina1 aGiannotti, Fosca1 aGabrielli, Lorenzo  uhttps://link.springer.com/chapter/10.1007/978-3-030-44584-3_2201528nas a2200145   4500008004100000245009700041210006900138260000900207520102400216100001901240700002101259700002301280700002101303856005801324       2020                        eng d00aEstimating countries’ peace index through the lens of the world news as monitored by GDELT0 aEstimating countries peace index through the lens of the world n  c20203 aPeacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying machine learning techniques, we demonstrate that news media attention, sentiment, and social stability from GDELT can be used as proxies for measuring GPI at a monthly level. Additionally, through the variable importance analysis, we show that each country's socio-economic, political, and military profile emerges. This could bring added value to researchers interested in "Data Science for Social Good", to policy-makers, and peacekeeping organizations since they could monitor peacefulness almost real-time, and therefore facilitate timely and more efficient policy-making.
1 aVoukelatou, V.1 aPappalardo, Luca1 aGabrielli, Lorenzo1 aGiannotti, Fosca  uhttps://ieeexplore.ieee.org/abstract/document/926005201787nas a2200313   4500008004100000020001400041245004600055210004500101260001500146300001100161520090000172100001701072700002301089700002301112700002101135700001701156700002101173700002301194700002001217700001401237700002901251700002101280700002301301700002001324700002001344700002301364700001901387856006701406       2020                        eng d  a2364-416800aHuman migration: the big data perspective0 aHuman migration the big data perspective  c2020/03/23  a1–203 aHow can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.1 aSirbu, Alina1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aConti, Marco1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aBertoli, Simone1 aKim, Jisu1 aMuntean, Cristina, Ioana1 aPappalardo, Luca1 aPassarella, Andrea1 aPedreschi, Dino1 aPollacci, Laura1 aPratesi, Francesca1 aSharma, Rajesh  uhttps://link.springer.com/article/10.1007%2Fs41060-020-00213-502283nas a2200265   4500008004100000245013900041210006900180520142700249100001901676700001801695700002201713700001901735700002101754700002501775700001901800700001701819700002101836700002001857700002201877700001801899700002101917700002301938700001901961856003701980       2020                        eng d00aMobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown0 aMobile phone data analytics against the COVID19 epidemics in Ita3 aUnderstanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modelling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. We address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?1 aBonato, Pietro1 aCintia, Paolo1 aFabbri, Francesco1 aFadda, Daniele1 aGiannotti, Fosca1 aLopalco, Pier, Luigi1 aMazzilli, Sara1 aNanni, Mirco1 aPappalardo, Luca1 aPedreschi, Dino1 aPenone, Francesco1 aRinzivillo, S1 aRossetti, Giulio1 aSavarese, Marcello1 aTavoschi, Lara  uhttps://arxiv.org/abs/2004.1127802783nas a2200517   4500008004100000020002200041245008500063210006900148260005200217520120400269100002301473700002501496700002701521700002101548700002101569700001801590700002101608700002201629700001901651700002501670700002301695700002201718700002201740700002101762700001601783700002001799700002601819700002401845700002001869700002201889700002301911700002001934700001901954700002201973700002001995700002002015700002002035700001802055700001902073700002302092700001802115700002002133700002402153700002102177856006702198       2020                        eng d  a978-3-030-65965-300aPrediction and Explanation of Privacy Risk on Mobility Data with Neural Networks0 aPrediction and Explanation of Privacy Risk on Mobility Data with  aChambSpringer International Publishingc2020//3 aThe analysis of privacy risk for mobility data is a fundamental part of any privacy-aware process based on such data. Mobility data are highly sensitive. Therefore, the correct identification of the privacy risk before releasing the data to the public is of utmost importance. However, existing privacy risk assessment frameworks have high computational complexity. To tackle these issues, some recent work proposed a solution based on classification approaches to predict privacy risk using mobility features extracted from the data. In this paper, we propose an improvement of this approach by applying long short-term memory (LSTM) neural networks to predict the privacy risk directly from original mobility data. We empirically evaluate privacy risk on real data by applying our LSTM-based approach. Results show that our proposed method based on a LSTM network is effective in predicting the privacy risk with results in terms of F1 of up to 0.91. Moreover, to explain the predictions of our model, we employ a state-of-the-art explanation algorithm, Shap. We explore the resulting explanation, showing how it is possible to provide effective predictions while explaining them to the end-user.1 aNaretto, Francesca1 aPellungrini, Roberto1 aNardini, Franco, Maria1 aGiannotti, Fosca1 aKoprinska, Irena1 aKamp, Michael1 aAppice, Annalisa1 aLoglisci, Corrado1 aAntonie, Luiza1 aZimmermann, Albrecht1 aGuidotti, Riccardo1 aÖzgöbek, Özlem1 aRibeiro, Rita, P.1 aGavaldà, Ricard1 aGama, João1 aAdilova, Linara1 aKrishnamurthy, Yamuna1 aFerreira, Pedro, M.1 aMalerba, Donato1 aMedeiros, Ibéria1 aCeci, Michelangelo1 aManco, Giuseppe1 aMasciari, Elio1 aRas, Zbigniew, W.1 aChristen, Peter1 aNtoutsi, Eirini1 aSchubert, Erich1 aZimek, Arthur1 aMonreale, Anna1 aBiecek, Przemyslaw1 aRinzivillo, S1 aKille, Benjamin1 aLommatzsch, Andreas1 aGulla, Jon, Atle  uhttps://link.springer.com/chapter/10.1007/978-3-030-65965-3_3401574nas a2200193   4500008004100000020001400041245005500055210005400110260001600164300001100180490000800191520100500199100002301204700002301227700001801250700001901268700002101287856007201308       2020                        eng d  a0169-023X00aPRIMULE: Privacy risk mitigation for user profiles0 aPRIMULE Privacy risk mitigation for user profiles  c2020/01/01/  a1017860 v1253 aThe availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification.1 aPratesi, Francesca1 aGabrielli, Lorenzo1 aCintia, Paolo1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S0169023X1830534203169nas a2200469   4500008004100000245011000041210006900151520175800220100001801978700001901996700002102015700002102036700002102057700002002078700001802098700001902116700002202135700002202157700002302179700002102202700002702223700001802250700002202268700002102290700002402311700001902335700002202354700001902376700002202395700002302417700002102440700002202461700001902483700002902502700002702531700002302558700002002581700002302601700001802624700002002642856003702662       2020                        eng d00aThe relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy0 arelationship between human mobility and viral transmissibility d3 aWe describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.1 aCintia, Paolo1 aFadda, Daniele1 aGiannotti, Fosca1 aPappalardo, Luca1 aRossetti, Giulio1 aPedreschi, Dino1 aRinzivillo, S1 aBonato, Pietro1 aFabbri, Francesco1 aPenone, Francesco1 aSavarese, Marcello1 aChecchi, Daniele1 aChiaromonte, Francesca1 aVineis, Paolo1 aGuzzetta, Giorgio1 aRiccardo, Flavia1 aMarziano, Valentina1 aPoletti, Piero1 aTrentini, Filippo1 aBella, Antonio1 aAndrianou, Xanthi1 aDel Manso, Martina1 aFabiani, Massimo1 aBellino, Stefania1 aBoros, Stefano1 aUrdiales, Alberto, Mateo1 aVescio, Maria, Fenicia1 aBrusaferro, Silvio1 aRezza, Giovanni1 aPezzotti, Patrizio1 aAjelli, Marco1 aMerler, Stefano  uhttps://arxiv.org/abs/2006.0314102108nas a2200217   4500008004100000245005300041210005100094520145500145100002301600700002301623700002101646700002201667700001801689700002001707700002101727700002101748700002201769700002301791700001101814856006501825       2020                        eng d00a(So) Big Data and the transformation of the city0 aSo Big Data and the transformation of the city3 aThe exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aCaldarelli, Guido1 aCintia, Paolo1 aCresci, Stefano1 aFacchini, Angelo1 aGiannotti, Fosca1 aGionis, Aristides1 aGuidotti, Riccardo1 aothers  uhttps://link.springer.com/article/10.1007/s41060-020-00207-301975nas a2200157   4500008004100000245009800041210006900139300001300208490000700221520143200228100001701660700002001677700002101697700002101718856007801739       2019                        eng d00aAlgorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model0 aAlgorithmic bias amplifies opinion fragmentation and polarizatio  ae02132460 v143 aThe flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population.1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca1 aKertész, János  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.021324601554nas a2200157   4500008004100000245007400041210006900115520102800184100002301212700001901235700002101254700002001275700002401295700001901319856005801338       2019                        eng d00aFactual and Counterfactual Explanations for Black Box Decision Making0 aFactual and Counterfactual Explanations for Black Box Decision M3 aThe rise of sophisticated machine learning models has brought accurate but obscure decision systems, which hide their logic, thus undermining transparency, trust, and the adoption of artificial intelligence (AI) in socially sensitive and safety-critical contexts. We introduce a local rule-based explanation method, providing faithful explanations of the decision made by a black box classifier on a specific instance. The proposed method first learns an interpretable, local classifier on a synthetic neighborhood of the instance under investigation, generated by a genetic algorithm. Then, it derives from the interpretable classifier an explanation consisting of a decision rule, explaining the factual reasons of the decision, and a set of counterfactuals, suggesting the changes in the instance features that would lead to a different outcome. Experimental results show that the proposed method outperforms existing approaches in terms of the quality of the explanations and of the accuracy in mimicking the black box.1 aGuidotti, Riccardo1 aMonreale, Anna1 aGiannotti, Fosca1 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://ieeexplore.ieee.org/abstract/document/892013801943nas a2200157   4500008004100000245006100041210006100102520143800163100002001601700002101621700002301642700001901665700002401684700001901708856005801727       2019                        eng d00aMeaningful explanations of Black Box AI decision systems0 aMeaningful explanations of Black Box AI decision systems3 aBlack box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box.1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://aaai.org/ojs/index.php/AAAI/article/view/505001999nas a2200181   4500008004100000245011100041210006900152300001100221490000700232520140700239100002101646700001801667700002101685700002301706700002001729700002101749856004701770       2019                        eng d00aPlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach0 aPlayeRank datadriven performance evaluation and player ranking i  a1–270 v103 aThe problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players’ evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank—i.e. searching players and player versatility—showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.1 aPappalardo, Luca1 aCintia, Paolo1 aFerragina, Paolo1 aMassucco, Emanuele1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://dl.acm.org/doi/abs/10.1145/334317201689nas a2200193   4500008004100000245007700041210006900118300001100187490000600198520109400204100002101298700001801319700001901337700002301356700002101379700002001400700002101420856005401441       2019                        eng d00aA public data set of spatio-temporal match events in soccer competitions0 apublic data set of spatiotemporal match events in soccer competi  a1–150 v63 aSoccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.1 aPappalardo, Luca1 aCintia, Paolo1 aRossi, Alessio1 aMassucco, Emanuele1 aFerragina, Paolo1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.nature.com/articles/s41597-019-0247-700404nas a2200121   4500008004100000245004000041210004000081100001700121700002100138700002000159700002100179856008200200       2019                        eng d00aPublic opinion and Algorithmic bias0 aPublic opinion and Algorithmic bias1 aSirbu, Alina1 aGiannotti, Fosca1 aPedreschi, Dino1 aKertész, János  uhttps://ercim-news.ercim.eu/en116/special/public-opinion-and-algorithmic-bias00434nas a2200109   4500008004100000245004800041210004800089100002000137700002200157700002100179856012400200       2019                        eng d00aTransparency in Algorithmic Decision Making0 aTransparency in Algorithmic Decision Making1 aRauber, Andreas1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://ercim-news.ercim.eu/en116/special/transparency-in-algorithmic-decision-making-introduction-to-the-special-theme01882nas a2200157   4500008004100000245006300041210006300104300000700167490000600174520139900180100001901579700002101598700002001619700002101639856006401660       2018                        eng d00aActive and passive diffusion processes in complex networks0 aActive and passive diffusion processes in complex networks  a420 v33 aIdeas, information, viruses: all of them, with their mechanisms, spread over the complex social information, viruses: all tissues described by our interpersonal relations. Usually, to simulate and understand the unfolding of such complex phenomena are used general mathematical models; these models act agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such degree of abstraction makes it easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, incorrect, simulation outcomes. In this work we introduce the concepts of active and passive diffusion to discriminate the degree in which individuals choice affect the overall spreading of content over a social graph. Moving from the analysis of a well-known passive diffusion schema, the Threshold model (that can be used to model peer-pressure related processes), we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation.

Our analysis, performed both in synthetic and real-world data, underline that the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches to capture the real complexity of the simulated system better.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/article/10.1007/s41109-018-0100-501271nas a2200145   4500008004100000245006500041210006300106260003600169520077200205100001900977700002100996700002001017700002101037856006701058       2018                        eng d00aDiffusive Phenomena in Dynamic Networks: a data-driven study0 aDiffusive Phenomena in Dynamic Networks a datadriven study  aBoston March 5-8 2018bSpringer3 aEveryday, ideas, information as well as viruses spread over complex social tissues described by our interpersonal relations. So far, the network contexts upon which diffusive phenomena unfold have usually considered static, composed by a fixed set of nodes and edges. Recent studies describe social networks as rapidly changing topologies. In this work – following a data-driven approach – we compare the behaviors of classical spreading models when used to analyze a given social network whose topological dynamics are observed at different temporal-granularities. Our goal is to shed some light on the impacts that the adoption of a static topology has on spreading simulations as well as to provide an alternative formulation of two classical diffusion models.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/chapter/10.1007/978-3-319-73198-8_1301603nas a2200193   4500008004100000245007700041210006900118260001300187520097300200100002301173700001901196700002101215700001701236700002401253700002001277700002101297700002401318856006701342       2018                        eng d00aDiscovering Mobility Functional Areas: A Mobility Data Analysis Approach0 aDiscovering Mobility Functional Areas A Mobility Data Analysis A  bSpringer3 aHow do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained.1 aGabrielli, Lorenzo1 aFadda, Daniele1 aRossetti, Giulio1 aNanni, Mirco1 aPiccinini, Leonardo1 aPedreschi, Dino1 aGiannotti, Fosca1 aLattarulo, Patrizia  uhttps://link.springer.com/chapter/10.1007/978-3-319-73198-8_2702705nas a2200181   4500008004100000245007900041210006900120260001200189300000600201490000600207520211900213100002302332700002302355700001902378700002002397700002102417856008502438       2018                        eng d00aDiscovering temporal regularities in retail customers’ shopping behavior0 aDiscovering temporal regularities in retail customers shopping b  c01/2018  a60 v73 aIn this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customer’s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.1 aGuidotti, Riccardo1 aGabrielli, Lorenzo1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0133-001229nas a2200157   4500008004100000245008100041210006900122260001300191520069500204100002000899700002300919700002100942700002100963700002000984856006701004       2018                        eng d00aThe Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis0 aFractal Dimension of Music Geography Popularity and Sentiment An  bSpringer3 aNowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a “fractal” musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians’ popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1901581nas a2200397   4500008004100000020002200041245009100063210006900154260004400223300001400267520041400281100001400695700001600709700001700725700001400742700001500756700001600771700002100787700001900808700001700827700001500844700002100859700002000880700002300900700001600923700001800939700002100957700002400978700001901002700001601021700001901037700001801056700001901074700002101093856006901114       2018                        eng d  a978-3-319-61893-700aHow Data Mining and Machine Learning Evolved from Relational Data Base to Data Science0 aHow Data Mining and Machine Learning Evolved from Relational Dat  aChambSpringer International Publishing  a287 - 3063 aDuring the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.1 aAmato, G.1 aCandela, L.1 aCastelli, D.1 aEsuli, A.1 aFalchi, F.1 aGennaro, C.1 aGiannotti, Fosca1 aMonreale, Anna1 aNanni, Mirco1 aPagano, P.1 aPappalardo, Luca1 aPedreschi, Dino1 aPratesi, Francesca1 aRabitti, F.1 aRinzivillo, S1 aRossetti, Giulio1 aRuggieri, Salvatore1 aSebastiani, F.1 aTesconi, M.1 aFlesca, Sergio1 aGreco, Sergio1 aMasciari, Elio1 aSaccà, Domenico  uhttps://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_1701364nas a2200157   4500008004100000245003700041210003300078300001100111520091500122100002001037700002301057700002101080700002101101700002001122856006401142       2018                        eng d00aThe italian music superdiversity0 aitalian music superdiversity  a1–233 aGlobalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs’ melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007/s11042-018-6511-600538nas a2200145   4500008004100000245006400041210006300105100002300168700001900191700002400210700002000234700001900254700002100273856009800294       2018                        eng d00aLocal Rule-Based Explanations of Black Box Decision Systems0 aLocal RuleBased Explanations of Black Box Decision Systems1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aPedreschi, Dino1 aTurini, Franco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/local-rule-based-explanations-black-box-decision-systems01236nas a2200181   4500008004100000245009100041210006900132300001200201490000600213520065500219100002100874700001900895700001800914700001700932700002000949700002100969856006400990       2018                        eng d00aNDlib: a python library to model and analyze diffusion processes over complex networks0 aNDlib a python library to model and analyze diffusion processes   a61–790 v53 aNowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground. To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.1 aRossetti, Giulio1 aMilli, Letizia1 aRinzivillo, S1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/article/10.1007/s41060-017-0086-600599nas a2200157   4500008004100000245007700041210006900118100002000187700002100207700002300228700001900251700002100270700002400291700001900315856010700334       2018                        eng d00aOpen the Black Box Data-Driven Explanation of Black Box Decision Systems0 aOpen the Black Box DataDriven Explanation of Black Box Decision 1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aMonreale, Anna1 aPappalardo, Luca1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/open-black-box-data-driven-explanation-black-box-decision-systems01676nas a2200145   4500008004100000245008600041210006900127520117000196100002301366700002101389700002101410700002101431700002001452856005801472       2018                        eng d00aPersonalized Market Basket Prediction with Temporal Annotated Recurring Sequences0 aPersonalized Market Basket Prediction with Temporal Annotated Re3 aNowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPappalardo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://ieeexplore.ieee.org/abstract/document/847715701950nas a2200181   4500008004100000245008800041210006900129260001200198490000700210520137400217100002301591700001901614700002201633700002101655700002001676700002401696856004801720       2018                        eng d00aPRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems0 aPRUDEnce a system for assessing privacy risk vs utility in data   c08/20180 v113 aData describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people’s whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data.1 aPratesi, Francesca1 aMonreale, Anna1 aTrasarti, Roberto1 aGiannotti, Fosca1 aPedreschi, Dino1 aYanagihara, Tadashi  uhttp://www.tdp.cat/issues16/tdp.a284a17.pdf02707nas a2200145   4500008004100000245005000041210004700091260006400138520221100202100002102413700002202434700002202456700002002478856006302498       2018                        eng d00aSoBigData: Social Mining & Big Data Ecosystem0 aSoBigData Social Mining Big Data Ecosystem  bInternational World Wide Web Conferences Steering Committee3 aOne of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by "big data". The research community uses the SoBigData facilities as a "secure digital wind-tunnel" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.1 aGiannotti, Fosca1 aTrasarti, Roberto1 aBontcheva, Kalina1 aGrossi, Valerio  uhttp://www.sobigdata.eu/sites/default/files/www%202018.pdf01683nas a2200181   4500008004100000245005600041210005400097300000700151490000700158520116300165100002301328700001901351700002401370700001901394700002101413700002001434856004701454       2018                        eng d00aA survey of methods for explaining black box models0 asurvey of methods for explaining black box models  a930 v513 aIn recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aTurini, Franco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://dl.acm.org/doi/abs/10.1145/323600902012nas a2200157   4500008004100000245005800041210005800099520153700157100001801694700002101712700001901733700002001752700002101772700001101793856005001804       2017                        eng d00aAuthenticated Outlier Mining for Outsourced Databases0 aAuthenticated Outlier Mining for Outsourced Databases3 aThe Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records (ARs) into the dataset, from which it constructs a set of artificial outliers (AOs) and artificial non-outliers (ANOs). The AOs and ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify ANOs and AOs without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.1 aDong, Boxiang1 aWang, Hui, Wendy1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, W  uhttps://ieeexplore.ieee.org/document/8048342/01663nas a2200157   4500008004100000245006500041210006500106260000800171520113100179100002301310700001901333700001701352700002101369700002001390856009501410       2017                        eng d00aClustering Individual Transactional Data for Masses of Users0 aClustering Individual Transactional Data for Masses of Users  bACM3 aMining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans1 aGuidotti, Riccardo1 aMonreale, Anna1 aNanni, Mirco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/clustering-individual-transactional-data-masses-users02550nas a2200133   4500008004100000245005900041210005800100260002400158520208400182100001902266700002102285700002002306856009002326       2017                        eng d00aData Science a Game-changer for Science and Innovation0 aData Science a Gamechanger for Science and Innovation  bG7 Academyc03/20173 aDigital technology is ubiquitous and very much part of public and private organizations and of individuals’ lives. People and things are becoming increasingly interconnected. Smartphones, smart buildings, smart factories, smart cities, autonomous vehicles and other smart environments and devices are filled with digital sensors, all of them creating an abundance of data. Governance and health care collect, generate and use data in an unprecedented quantity. New high- throughput scientific instruments and methods, like telescopes, satellites, accelerators, supercomputers, sensor networks and gene sequencing methods as well as large scale simulations generate massive amounts of data. Often referred to as data deluge, or Big Data, massive datasets revolutionize the way research is carried out, resulting in the emergence of a new, fourth paradigm of science based on data-intensive computing and data driven discovery4. Accordingly, the path to the solution of the problem of sustainable development will lead through Big Data, as maintaining the whole complexity of our modern society, including communication and traffic services, manufacturing, trade and commerce, financial services, health security, science, education and policy making requires this novel approach.
The new availability of huge amounts of data, along with advanced tools of exploratory data analysis, data mining/machine learning, and data visualization, and scalable infrastructures, has produced a spectacular change in the scientific method: all this is Data Science. This paper describes the main issues around Data Science as it will play out in the coming years in science and society. It focus on the scientific, technical and ethical challenges (A), on its role for disruptive innovation for science, industry, policy and people (B), on its scientific, technological and educational challenges (C) and finally, on the quantitative expectations of its economic impact (D). In our work we could count on many reports and studies on the subject, particularly on the BDVA5 and ERCIM6 reports.1 aBeltram, Fabio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/data-science-game-changer-science-and-innovation01579nas a2200157   4500008004100000245007400041210006900115300001300184490000700197520103200204100002101236700001901257700002101276700002001297856010401317       2017                        eng d00aForecasting success via early adoptions analysis: A data-driven study0 aForecasting success via early adoptions analysis A datadriven st  ae01890960 v123 aInnovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don’t. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.1 aRossetti, Giulio1 aMilli, Letizia1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/forecasting-success-early-adoptions-analysis-data-driven-study01239nas a2200157   4500008004100000245008100041210006900122260001900191520069900210100002000909700002300929700002100952700002100973700002000994856006701014       2017                        eng d00aThe Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis0 aFractal Dimension of Music Geography Popularity and Sentiment An  bSpringer, Cham3 aNowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a “fractal” musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians’ popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.

1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1901602nas a2200157   4500008004100000245007000041210006800111490000700179520110500186100002201291700001801313700002101331700002001352700002201372856005001394       2017                        eng d00aHyWare: a HYbrid Workflow lAnguage for Research E-infrastructures0 aHyWare a HYbrid Workflow lAnguage for Research Einfrastructures0 v233 aResearch e-infrastructures are "systems of systems", patchworks of tools, services and data sources, evolving over time to address the needs of the scientific process. Accordingly, in such environments, researchers implement their scientific processes by means of workflows made of a variety of actions, including for example usage of web services, download and execution of shared software libraries or tools, or local and manual manipulation of data. Although scientists may benefit from sharing their scientific process, the heterogeneity underpinning e-infrastructures hinders their ability to represent, share and eventually reproduce such workflows. This work presents HyWare, a language for representing scientific process in highly-heterogeneous e-infrastructures in terms of so-called hybrid workflows. HyWare lays in between "business process modeling languages", which offer a formal and high-level description of a reasoning, protocol, or procedure, and "workflow execution languages", which enable the fully automated execution of a sequence of computational steps via dedicated engines.1 aCandela, Leonardo1 aManghi, Paolo1 aGiannotti, Fosca1 aGrossi, Valerio1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1045/january2017-candela01474nas a2200145   4500008004100000245007600041210006900117260001300186520098100199100001901180700002101199700002001220700002101240856006701261       2017                        eng d00aInformation diffusion in complex networks: The active/passive conundrum0 aInformation diffusion in complex networks The activepassive conu  bSpringer3 aIdeas, information, viruses: all of them, with their mechanisms, can spread over the complex social tissues described by our interpersonal relations. Classical spreading models can agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such simplification makes easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, partial, simulation outcomes. In this work we discuss the concepts of active and passive diffusion: moving from analysis of a well-known passive model, the Threshold one, we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation. Our data-driven analysis shows how, in such context, the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/chapter/10.1007/978-3-319-72150-7_2501536nas a2200157   4500008004100000245008700041210006900128260000900197520094200206100002301148700002101171700002101192700002101213700002001234856012401254       2017                        eng d00aMarket Basket Prediction using User-Centric Temporal Annotated Recurring Sequences0 aMarket Basket Prediction using UserCentric Temporal Annotated Re  bIEEE3 aNowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer’s decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern named Temporal Annotated Recurring Sequence (TARS). We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer’s stocks and recommend the set of most necessary items. A deep experimentation shows that TARS can explain the customers’ purchase behavior, and that TBP outperforms the state-of-the-art competitors.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPappalardo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/market-basket-prediction-using-user-centric-temporal-annotated-recurring-sequences00371nas a2200109   4500008004100000245005600041210005600097100001700153700002200170700002100192856004800213       2017                        eng d00aMovement Behaviour Recognition for Water Activities0 aMovement Behaviour Recognition for Water Activities1 aNanni, Mirco1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://doi.org/10.1007/978-3-319-71970-2_701609nas a2200169   4500008004100000245005400041210005300095260001200148300001400160490000700174520108700181100002201268700002301290700001901313700002101332856008601353       2017                        eng d00aMyWay: Location prediction via mobility profiling0 aMyWay Location prediction via mobility profiling  c03/2017  a350–3670 v643 aForecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user׳s movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.1 aTrasarti, Roberto1 aGuidotti, Riccardo1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/myway-location-prediction-mobility-profiling01274nas a2200169   4500008004100000245009100041210006900132300001100201520065400212100002100866700001900887700001800906700001700924700002000941700002100961856012200982       2017                        eng d00aNDlib: a python library to model and analyze diffusion processes over complex networks0 aNDlib a python library to model and analyze diffusion processes   a1–193 aNowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground.To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.1 aRossetti, Giulio1 aMilli, Letizia1 aRinzivillo, S1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/ndlib-python-library-model-and-analyze-diffusion-processes-over-complex-networks01119nas a2200169   4500008004100000245004700041210004600088260001000134520063100144100002100775700001900796700001800815700001700833700002000850700002100870856005800891       2017                        eng d00aNDlib: Studying Network Diffusion Dynamics0 aNDlib Studying Network Diffusion Dynamics  aTokyo3 aNowadays the analysis of diffusive phenomena occurring on top of complex networks represents a hot topic in the Social Network Analysis playground. In order to support students, teachers, developers and researchers in this work we introduce a novel simulation framework, ND LIB . ND LIB is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. Upon the diffusion library, we designed a simulation server that allows remote execution of experiments and an online visualization tool that abstract the programmatic interface and makes available the simulation platform to non-technicians.1 aRossetti, Giulio1 aMilli, Letizia1 aRinzivillo, S1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://ieeexplore.ieee.org/abstract/document/825977401912nas a2200169   4500008004100000245006500041210006400106300001400170490000700184520135600191100002301547700001701570700001801587700002001605700002101625856009601646       2017                        eng d00aNever drive alone: Boosting carpooling with network analysis0 aNever drive alone Boosting carpooling with network analysis  a237–2570 v643 aCarpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling.1 aGuidotti, Riccardo1 aNanni, Mirco1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/never-drive-alone-boosting-carpooling-network-analysis01626nas a2200145   4500008004100000245006300041210006300104520117000167100002301337700002101360700002101381700002101402700002001423856003701443       2017                        eng d00aNext Basket Prediction using Recurring Sequential Patterns0 aNext Basket Prediction using Recurring Sequential Patterns3 aNowadays, a hot challenge for supermarket chains is to offer personalized services for their customers. Next basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable to capture at the same time the different factors influencing the customer's decision process: co-occurrency, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPappalardo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://arxiv.org/abs/1702.0715801685nas a2200145   4500008004100000245008500041210006900126300001200195490000600207520118800213100002101401700002001422700002101442856007601463       2017                        eng d00aNode-centric Community Discovery: From static to dynamic social network analysis0 aNodecentric Community Discovery From static to dynamic social ne  a32–480 v33 aNowadays, online social networks represent privileged playgrounds that enable researchers to study, characterize and understand complex human behaviors. Social Network Analysis, commonly known as SNA, is the multidisciplinary field of research under which researchers of different backgrounds perform their studies: one of the hottest topics in such diversified context is indeed Community Discovery. Clustering individuals, whose relations are described by a networked structure, into homogeneous communities is a complex task required by several analytical processes. Moreover, due to the user-centric and dynamic nature of online social services, during the last decades, particular emphasis was dedicated to the definition of node-centric, overlapping and evolutive Community Discovery methodologies.
In this paper we provide a comprehensive and concise review of the main results, both algorithmic and analytical, we obtained in this field. Moreover, to better underline the rationale behind our research activity on Community Discovery, in this work we provide a synthetic review of the relevant literature, discussing not only methodological results but also analytical ones.1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/abs/pii/S246869641730105201373nas a2200145   4500008004100000245005000041210005000091260001300141520092300154100002301077700001901100700002101119700002001140856006701160       2017                        eng d00aPrivacy Preserving Multidimensional Profiling0 aPrivacy Preserving Multidimensional Profiling  bSpringer3 aRecently, big data had become central in the analysis of human behavior and the development of innovative services. In particular, a new class of services is emerging, taking advantage of different sources of data, in order to consider the multiple aspects of human beings. Unfortunately, these data can lead to re-identification problems and other privacy leaks, as diffusely reported in both scientific literature and media. The risk is even more pressing if multiple sources of data are linked together since a potential adversary could know information related to each dataset. For this reason, it is necessary to evaluate accurately and mitigate the individual privacy risk before releasing personal data. In this paper, we propose a methodology for the first task, i.e., assessing privacy risk, in a multidimensional scenario, defining some possible privacy attacks and simulating them using real-world datasets.1 aPratesi, Francesca1 aMonreale, Anna1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1501783nas a2200169   4500008004100000245009100041210006900132260001300201520120400214100002001418700001701438700002101455700002001476700002201496700002901518856006601547       2017                        eng d00aSentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter0 aSentiment Spreading An Epidemic Model for LexiconBased Sentiment  bSpringer3 aWhile sentiment analysis has received significant attention in the last years, problems still exist when tools need to be applied to microblogging content. This because, typically, the text to be analysed consists of very short messages lacking in structure and semantic context. At the same time, the amount of text produced by online platforms is enormous. So, one needs simple, fast and effective methods in order to be able to efficiently study sentiment in these data. Lexicon-based methods, which use a predefined dictionary of terms tagged with sentiment valences to evaluate sentiment in longer sentences, can be a valid approach. Here we present a method based on epidemic spreading to automatically extend the dictionary used in lexicon-based sentiment analysis, starting from a reduced dictionary and large amounts of Twitter data. The resulting dictionary is shown to contain valences that correlate well with human-annotated sentiment, and to produce tweet sentiment classifications comparable to the original dictionary, with the advantage of being able to tag more tweets than the original. The method is easily extensible to various languages and applicable to large amounts of data.1 aPollacci, Laura1 aSirbu, Alina1 aGiannotti, Fosca1 aPedreschi, Dino1 aLucchese, Claudio1 aMuntean, Cristina, Ioana  uhttps://link.springer.com/chapter/10.1007/978-3-319-70169-1_900584nas a2200145   4500008004100000245009100041210006900132260001600201100002300217700002200240700001700262700002100279700002000300856011800320       2017                        eng d00aThere's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas0 aTheres A Path For Everyone A DataDriven Personal Model Reproduci  aTokyobIEEE1 aGuidotti, Riccardo1 aTrasarti, Roberto1 aNanni, Mirco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/theres-path-everyone-data-driven-personal-model-reproducing-mobility-agendas01804nas a2200157   4500008004100000245008200041210006900123300001600192490000800208520128300216100002101499700002101520700002001541700002101561856006401582       2017                        eng d00aTiles: an online algorithm for community discovery in dynamic social networks0 aTiles an online algorithm for community discovery in dynamic soc  a1213–12410 v1063 aCommunity discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios static community discovery fails to identify a partition of the graph that is semantically consistent with the temporal information expressed by the data. In this work we propose Tiles, an algorithm that extracts overlapping communities and tracks their evolution in time following an online iterative procedure. Our algorithm operates following a domino effect strategy, dynamically recomputing nodes community memberships whenever a new interaction takes place. We compare Tiles with state-of-the-art community detection algorithms on both synthetic and real world networks having annotated community structure: our experiments show that the proposed approach is able to guarantee lower execution times and better correspondence with the ground truth communities than its competitors. Moreover, we illustrate the specifics of the proposed approach by discussing the properties of identified communities it is able to identify.1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/article/10.1007/s10994-016-5582-801717nas a2200181   4500008004100000245007400041210006900115300001200184490000600196520110000202100002101302700002101323700002301344700002201367700002001389700002101409856010501430       2016                        eng d00aAn analytical framework to nowcast well-being using mobile phone data0 aanalytical framework to nowcast wellbeing using mobile phone dat  a75–920 v23 aAn intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users’ trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly “nowcast” the well-being and the socio-economic development of a territory.1 aPappalardo, Luca1 aVanhoof, Maarten1 aGabrielli, Lorenzo1 aSmoreda, Zbigniew1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/analytical-framework-nowcast-well-being-using-mobile-phone-data01199nas a2200289   4500008004100000022004100041050001400082245004600096210004500142260001200187300000800199490000600207520037500213100002300588700002200611700002300633700002200656700002100678700002000699700002400719700001900743700002000762700002000782700002100802700002400823856006200847       2016                        eng d  aprint: 2095-8099 / online: 2096-0026  a10-1244/N00aBig Data Research in Italy: A Perspective0 aBig Data Research in Italy A Perspective  c06/2016  a1630 v23 aThe aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.1 aBergamaschi, Sonia1 aCarlini, Emanuele1 aCeci, Michelangelo1 aFurletti, Barbara1 aGiannotti, Fosca1 aMalerba, Donato1 aMezzanzanica, Mario1 aMonreale, Anna1 aPasi, Gabriella1 aPedreschi, Dino1 aPerego, Raffaele1 aRuggieri, Salvatore  uhttp://engineering.org.cn/EN/abstract/article_12288.shtml01559nas a2200181   4500008004100000245009300041210006900134300000800203490000600211520091800217100002101135700002101156700001701177700002001194700002101214700001801235856012401253       2016                        eng d00aHomophilic network decomposition: a community-centric analysis of online social services0 aHomophilic network decomposition a communitycentric analysis of   a1030 v63 aIn this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks—Skype, LastFM and Google+—and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+.1 aRossetti, Giulio1 aPappalardo, Luca1 aKikas, Riivo1 aPedreschi, Dino1 aGiannotti, Fosca1 aDumas, Marlon  uhttps://kdd.isti.cnr.it/publications/homophilic-network-decomposition-community-centric-analysis-online-social-services01970nas a2200193   4500008004100000022001400041245010300055210006900158260001200227300000700239490000600246520137200252100002101624700002301645700001901668700002001687700002101707856004801728       2016                        eng d  a1869-546900aA supervised approach for intra-/inter-community interaction prediction in dynamic social networks0 asupervised approach for intraintercommunity interaction predicti  c09/2016  a860 v63 aDue to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intra-community and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.1 aRossetti, Giulio1 aGuidotti, Riccardo1 aMiliou, Ioanna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1007/s13278-016-0397-y01050nas a2200157   4500008004100000245004700041210004700088260003800135300001400173520054400187100002100731700002300752700002000775700001800795856007900813       2016                        eng d00aUnderstanding human mobility with big data0 aUnderstanding human mobility with big data  bSpringer International Publishing  a208–2203 aThe paper illustrates basic methods of mobility data mining, designed to extract from the big mobility data the patterns of collective movement behavior, i.e., discover the subgroups of travelers characterized by a common purpose, profiles of individual movement activity, i.e., characterize the routine mobility of each traveler. We illustrate a number of concrete case studies where mobility data mining is put at work to create powerful analytical services for policy makers, businesses, public administrations, and individual citizens.1 aGiannotti, Fosca1 aGabrielli, Lorenzo1 aPedreschi, Dino1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/understanding-human-mobility-big-data01716nas a2200169   4500008004100000245006700041210006700108300000700175490000600182520115300188100002301341700001901364700001801383700002001401700002101421856010401442       2016                        eng d00aUnveiling mobility complexity through complex network analysis0 aUnveiling mobility complexity through complex network analysis  a590 v63 aThe availability of massive digital traces of individuals is offering a series of novel insights on the understanding of patterns characterizing human mobility. Many studies try to semantically enrich mobility data with annotations about human activities. However, these approaches either focus on places with high frequencies (e.g., home and work), or relay on background knowledge (e.g., public available points of interest). In this paper, we depart from the concept of frequency and we focus on a high level representation of mobility using network analytics. The visits of each driver to each systematic destination are modeled as links in a bipartite network where a set of nodes represents drivers and the other set represents places. We extract such network from two real datasets of human mobility based, respectively, on GPS and GSM data. We introduce the concept of mobility complexity of drivers and places as a ranking analysis over the nodes of these networks. In addition, by means of community discovery analysis, we differentiate subgroups of drivers and places according both to their homogeneity and to their mobility complexity.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/unveiling-mobility-complexity-through-complex-network-analysis01157nas a2200157   4500008004100000245005600041210005300097260003600150520061100186100002300797700002200820700002200842700002100864700002000885856009400905       2015                        eng d00aCity users’ classification with mobile phone data0 aCity users classification with mobile phone data  aSanta Clara (CA) - USAc11/20153 aNowadays mobile phone data are an actual proxy for studying the users’ social life and urban dynamics. In this paper we present the Sociometer, and analytical framework aimed at classifying mobile phone users into behavioral categories by means of their call habits. The analytical process starts from spatio-temporal profiles, learns the different behaviors, and returns annotated profiles. After the description of the methodology and its evaluation, we present an application of the Sociometer for studying city users of one small and one big city, evaluating the impact of big events in these cities.1 aGabrielli, Lorenzo1 aFurletti, Barbara1 aTrasarti, Roberto1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/city-users%E2%80%99-classification-mobile-phone-data00574nas a2200169   4500008004100000020002200041245007400063210006900137260002400206100002100230700002100251700001700272700002000289700002100309700001800330856005600348       2015                        eng d  a978-1-4503-3854-700aCommunity-centric analysis of user engagement in Skype social network0 aCommunitycentric analysis of user engagement in Skype social net  aParis, FrancebIEEE1 aRossetti, Giulio1 aPappalardo, Luca1 aKikas, Riivo1 aPedreschi, Dino1 aGiannotti, Fosca1 aDumas, Marlon  uhttp://dl.acm.org/citation.cfm?doid=2808797.280938402380nas a2200169   4500008004100000245004700041210004500088300001600133490000700149520190300156100001702059700002602076700001902102700002002121700002102141856004802162       2015                        eng d00aDiscrimination- and privacy-aware patterns0 aDiscrimination and privacyaware patterns  a1733–17820 v293 aData mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. We are therefore faced with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Consider the case when a set of patterns extracted from the personal data of a population of individual persons is released for a subsequent use into a decision making process, such as, e.g., granting or denying credit. First, the set of patterns may reveal sensitive information about individual persons in the training population and, second, decision rules based on such patterns may lead to unfair discrimination, depending on what is represented in the training cases. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a set of pattern sanitization methods, one for each discrimination measure used in the legal literature, to achieve a fair publishing of frequent patterns in combination with two possible privacy transformations: one based on k-anonymity and one based on differential privacy. Our proposed pattern sanitization methods based on k-anonymity yield both privacy- and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. Moreover, they obtain a better trade-off between protection and data quality than the sanitization methods based on differential privacy. Finally, the effectiveness of our proposals is assessed by extensive experiments. 1 aHajian, Sara1 aDomingo-Ferrer, Josep1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1007/s10618-014-0393-702029nas a2200145   4500008004100000245008700041210006900128520139000197100001801587700002101605700002001626700002101646700002001667856019601687       2015                        eng d00aThe harsh rule of the goals: data-driven performance indicators for football teams0 aharsh rule of the goals datadriven performance indicators for fo3 a—Sports analytics in general, and football (soccer in
USA) analytics in particular, have evolved in recent years in an
amazing way, thanks to automated or semi-automated sensing
technologies that provide high-fidelity data streams extracted
from every game. In this paper we propose a data-driven
approach and show that there is a large potential to boost the
understanding of football team performance. From observational
data of football games we extract a set of pass-based performance
indicators and summarize them in the H indicator. We observe a
strong correlation among the proposed indicator and the success
of a team, and therefore perform a simulation on the four major
European championships (78 teams, almost 1500 games). The
outcome of each game in the championship was replaced by a
synthetic outcome (win, loss or draw) based on the performance
indicators computed for each team. We found that the final
rankings in the simulated championships are very close to the
actual rankings in the real championships, and show that teams
with high ranking error show extreme values of a defense/attack
efficiency measure, the Pezzali score. Our results are surprising
given the simplicity of the proposed indicators, suggesting that
a complex systems’ view on football data has the potential of
revealing hidden patterns and behavior of superior quality.1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aMalvaldi, Marco  uhttps://www.researchgate.net/profile/Luca_Pappalardo/publication/281318318_The_harsh_rule_of_the_goals_data-driven_performance_indicators_for_football_teams/links/561668e308ae37cfe4090a5d.pdf01436nas a2200169   4500008004100000020002200041245007800063210006900141260002400210520086700234100002101101700002301122700002401145700002001169700002101189856005601210       2015                        eng d  a978-1-4503-3854-700aInteraction Prediction in Dynamic Networks exploiting Community Discovery0 aInteraction Prediction in Dynamic Networks exploiting Community   aParis, FrancebIEEE3 aDue to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.1 aRossetti, Giulio1 aGuidotti, Riccardo1 aPennacchioli, Diego1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dl.acm.org/citation.cfm?doid=2808797.280940102010nas a2200157   4500008004100000245004500041210004500086260001200131300001100143490000600154520154300160100002001703700002401723700002101747856008401768       2015                        eng d00aProduct assortment and customer mobility0 aProduct assortment and customer mobility  c10-2015  a1–180 v43 aCustomers mobility is dependent on the sophistication of their needs: sophisticated customers need to travel more to fulfill their needs. In this paper, we provide more detailed evidence of this phenomenon, providing an empirical validation of the Central Place Theory. For each customer, we detect what is her favorite shop, where she purchases most products. We can study the relationship between the favorite shop and the closest one, by recording the influence of the shop’s size and the customer’s sophistication in the discordance cases, i.e. the cases in which the favorite shop is not the closest one. We show that larger shops are able to retain most of their closest customers and they are able to catch large portions of customers from smaller shops around them. We connect this observation with the shop’s larger sophistication, and not with its other characteristics, as the phenomenon is especially noticeable when customers want to satisfy their sophisticated needs. This is a confirmation of the recent extensions of the Central Place Theory, where the original assumptions of homogeneity in customer purchase power and needs are challenged. Different types of shops have also different survival logics. The largest shops get closed if they are unable to catch customers from the smaller shops, while medium size shops get closed if they cannot retain their closest customers. All analysis are performed on a large real-world dataset recording all purchases from millions of customers across the west coast of Italy.1 aCoscia, Michele1 aPennacchioli, Diego1 aGiannotti, Fosca  uhttp://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0051-301921nas a2200169   4500008004100000245003800041210003800079260002400117520140400141100001901545700001901564700002101583700002001604700002101624700002501645856008101670       2015                        eng d00aQuantification in Social Networks0 aQuantification in Social Networks  aParis, FrancebIEEE3 aIn many real-world applications there is a need to monitor the distribution of a population across different classes, and to track changes in this distribution over time. As an example, an important task is to monitor the percentage of unemployed adults in a given region. When the membership of an individual in a class cannot be established deterministically, a typical solution is the classification task. However, in the above applications the final goal is not determining which class the individuals belong to, but estimating the prevalence of each class in the unlabeled data. This task is called quantification. Most of the work in the literature addressed the quantification problem considering data presented in conventional attribute format. Since the ever-growing availability of web and social media we have a flourish of network data representing a new important source of information and by using quantification network techniques we could quantify collective behavior, i.e., the number of users that are involved in certain type of activities, preferences, or behaviors. In this paper we exploit the homophily effect observed in many social networks in order to construct a quantifier for networked data. Our experiments show the effectiveness of the proposed approaches and the comparison with the existing state-of-the-art quantification methods shows that they are more accurate. 1 aMilli, Letizia1 aMonreale, Anna1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca1 aSebastiani, Fabrizio  uhttp://www.giuliorossetti.net/about/wp-content/uploads/2015/12/main_DSAA.pdf01571nas a2200181   4500008004100000245005600041210005600097260000700153490000600160520105400166100002101220700002001241700001801261700002001279700002101299700002801320856004101348       2015                        eng d00aReturners and explorers dichotomy in human mobility0 aReturners and explorers dichotomy in human mobility  c090 v63 aThe availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.1 aPappalardo, Luca1 aSimini, Filippo1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca1 aBarabasi, Albert-Laszlo  uhttp://dx.doi.org/10.1038/ncomms916601825nas a2200217   4500008004100000245004800041210004600089300000600135490000600141520119400147100001801341700001901359700002201378700002401400700002101424700002001445700002301465700001901488700002401507856007601531       2015                        eng d00aA risk model for privacy in trajectory data0 arisk model for privacy in trajectory data  a90 v23 aTime sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper, we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data and then, we show how the empirical evaluation of the privacy risk has a different trend in synthetic data describing random movements.1 aBasu, Anirban1 aMonreale, Anna1 aTrasarti, Roberto1 aCorena, Juan Camilo1 aGiannotti, Fosca1 aPedreschi, Dino1 aKiyomoto, Shinsaku1 aMiyake, Yutaka1 aYanagihara, Tadashi  uhttps://kdd.isti.cnr.it/publications/risk-model-privacy-trajectory-data00674nas a2200205   4500008004100000245006100041210006000102300001400162490000700176100002300183700002100206700002000227700002000247700002100267700002000288700001800308700002100326700002300347856009800370       2015                        eng d00aSmall Area Model-Based Estimators Using Big Data Sources0 aSmall Area ModelBased Estimators Using Big Data Sources  a263–2810 v311 aMarchetti, Stefano1 aGiusti, Caterina1 aPratesi, Monica1 aSalvati, Nicola1 aGiannotti, Fosca1 aPedreschi, Dino1 aRinzivillo, S1 aPappalardo, Luca1 aGabrielli, Lorenzo  uhttps://kdd.isti.cnr.it/publications/small-area-model-based-estimators-using-big-data-sources00458nas a2200121   4500008004100000245009600041210006900137100002300206700002200229700001700251700002100268856004700289       2015                        eng d00aTowards user-centric data management: individual mobility analytics for collective services0 aTowards usercentric data management individual mobility analytic1 aGuidotti, Riccardo1 aTrasarti, Roberto1 aNanni, Mirco1 aGiannotti, Fosca  uhttp://doi.acm.org/10.1145/2834126.283413201297nas a2200193   4500008004100000022002200041245006500063210006500128260003800193300001200231490000900243520068200252100002300934700002200957700002100979700001701000700001801017856006801035       2015                        eng d  a978-3-319-15200-400aUse of Mobile Phone Data to Estimate Visitors Mobility Flows0 aUse of Mobile Phone Data to Estimate Visitors Mobility Flows  bSpringer International Publishing  a214-2260 v89383 aBig Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data “proxies”, such as the mobile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality.1 aGabrielli, Lorenzo1 aFurletti, Barbara1 aGiannotti, Fosca1 aNanni, Mirco1 aRinzivillo, S  uhttp://link.springer.com/chapter/10.1007%2F978-3-319-15201-1_1401965nas a2200193   4500008004100000245007600041210006900117260000900186520136400195100001801559700002401577700001901601700002001620700002101640700002301661700002001684700001901704856004801723       2014                        eng d00aCF-inspired Privacy-Preserving Prediction of Next Location in the Cloud0 aCFinspired PrivacyPreserving Prediction of Next Location in the   bIEEE3 aMobility data gathered from location sensors such as Global Positioning System (GPS) enabled phones and vehicles is valuable for spatio-temporal data mining for various location-based services (LBS). Such data is often considered sensitive and there exist many a mechanism for privacy preserving analyses of the data. Through various anonymisation mechanisms, it can be ensured with a high probability that a particular individual cannot be identified when mobility data is outsourced to third parties for analysis. However, challenges remain with the privacy of the queries on outsourced analysis results, especially when the queries are sent directly to third parties by end-users. Drawing inspiration from our earlier work in privacy preserving collaborative filtering (CF) and next location prediction, in this exploratory work, we propose a novel representation of trajectory data in the CF domain and experiment with a privacy preserving Slope One CF predictor. We present evaluations for the accuracy and the computational performance of our proposal using anonymised data gathered from real traffic data in the Italian cities of Pisa and Milan. One use-case is a third-party location-prediction-as-a-service deployed on a public cloud, which can respond to privacy-preserving queries while enabling data owners to build a rich predictor on the cloud. 1 aBasu, Anirban1 aCorena, Juan Camilo1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aKiyomoto, Shinsaku1 aVaidya, Jaideep1 aMiyake, Yutaka  uhttp://dx.doi.org/10.1109/CloudCom.2014.11401871nas a2200217   4500008003900000022001400039245010000053210006900153300000600222520115600228653001901384100002201403700003101425700001701456700002201473700002201495700002101517700002201538700002201560856007101582       2014                          d  a0308-596100aDiscovering urban and country dynamics from mobile phone data with spatial correlation patterns0 aDiscovering urban and country dynamics from mobile phone data wi  a-3 aAbstract Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France.10aUrban dynamics1 aTrasarti, Roberto1 aOlteanu-Raimond, Ana-Maria1 aNanni, Mirco1 aCouronné, Thomas1 aFurletti, Barbara1 aGiannotti, Fosca1 aSmoreda, Zbigniew1 aZiemlicki, Cezary  uhttp://www.sciencedirect.com/science/article/pii/S030859611300201201701nas a2200157   4500008003900000245002700039210002700066300001400093520128600107100001701393700001901410700002001429700002601449700002101475856004701496       2014                          d00aFair pattern discovery0 aFair pattern discovery  a113–1203 aData mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. We are assisting to unprecedented opportunities of understanding human and society behavior that unfortunately is darkened by several risks for human rights: one of this is the unfair discrimination based on the extracted patterns and profiles. Consider the case when a set of patterns extracted from the personal data of a population of individual persons is released for subsequent use in a decision making process, such as, e.g., granting or denying credit. Decision rules based on such patterns may lead to unfair discrimination, depending on what is represented in the training cases. In this context, we address the discrimination risks resulting from publishing frequent patterns. We present a set of pattern sanitization methods, one for each discrimination measure used in the legal literature, for fair (discrimination-protected) publishing of frequent pattern mining results. Our proposed pattern sanitization methods yield discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. Finally, the effectiveness of our proposals is assessed by extensive experiments.1 aHajian, Sara1 aMonreale, Anna1 aPedreschi, Dino1 aDomingo-Ferrer, Josep1 aGiannotti, Fosca  uhttp://doi.acm.org/10.1145/2554850.255504301818nas a2200205   4500008003900000245002300039210002300062260001500085300000900100520128200109100001701391700002201408700001801430700002201448700001801470700002301488700001801511700002101529856006201550       2014                          d00aMobility Profiling0 aMobility Profiling  bIGI Global  a1-293 aThe ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. 1 aNanni, Mirco1 aTrasarti, Roberto1 aCintia, Paolo1 aFurletti, Barbara1 aRenso, Chiara1 aGabrielli, Lorenzo1 aRinzivillo, S1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mobility-profiling-000482nas a2200145   4500008004100000245006800041210006300109100002400172700002100196700002100217700002000238700002100258700002000279856003700299       2014                        eng d00aThe patterns of musical influence on the Last.Fm social network0 apatterns of musical influence on the LastFm social network1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttps://kdd.isti.cnr.it/node/62301645nas a2200205   4500008003900000245004500039210004300084300001400127520105800141100001801199700001901217700002401236700002101260700002001281700002301301700001901324700002401343700002201367856005001389       2014                          d00aA Privacy Risk Model for Trajectory Data0 aPrivacy Risk Model for Trajectory Data  a125–1403 aTime sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data.1 aBasu, Anirban1 aMonreale, Anna1 aCorena, Juan Camilo1 aGiannotti, Fosca1 aPedreschi, Dino1 aKiyomoto, Shinsaku1 aMiyake, Yutaka1 aYanagihara, Tadashi1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1007/978-3-662-43813-8_901574nas a2200157   4500008003900000245006200039210006000101490000700161520105400168100001901222700001801241700002301259700002101282700002001303856009301323       2014                          d00aPrivacy-by-Design in Big Data Analytics and Social Mining0 aPrivacybyDesign in Big Data Analytics and Social Mining0 v103 aPrivacy is ever-growing concern in our society and is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving human personal sensitive information. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. As a result, privacy preservation simply cannot be accomplished by de-identification alone. In this paper, we propose the privacy-by-design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start.1 aMonreale, Anna1 aRinzivillo, S1 aPratesi, Francesca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/privacy-design-big-data-analytics-and-social-mining00505nas a2200145   4500008004100000245008100041210006900122100001800191700002300209700001700232700002100249700002000270700002100290856004800311       2014                        eng d00aThe purpose of motion: Learning activities from Individual Mobility Networks0 apurpose of motion Learning activities from Individual Mobility N1 aRinzivillo, S1 aGabrielli, Lorenzo1 aNanni, Mirco1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/DSAA.2014.705809002340nas a2200169   4500008004100000245004200041210003800083300001100121490000600132520186000138100002401998700002002022700001802042700002102060700002002081856006902101       2014                        eng d00aThe retail market as a complex system0 aretail market as a complex system  a1–270 v33 aAim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country’s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products’ volumes of sales with the customers’ volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://link.springer.com/article/10.1140/epjds/s13688-014-0033-x01299nas a2200169   4500008004100000245006600041210006600107260003800173300001400211520076600225100002300991700001901014700001801033700002001051700002101071856003701092       2014                        eng d00aRetrieving Points of Interest from Human Systematic Movements0 aRetrieving Points of Interest from Human Systematic Movements  bSpringer International Publishing  a294–3083 aHuman mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers’ systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/node/63602438nas a2200157   4500008004100000245008400041210006900125300000600194490000600200520195300206100002002159700002102179700002102200700002002221856003902241       2014                        eng d00aUncovering Hierarchical and Overlapping Communities with a Local-First Approach0 aUncovering Hierarchical and Overlapping Communities with a Local  a60 v93 aCommunity discovery in complex networks is the task of organizing a network’s structure by grouping together nodes related to each other. Traditional approaches are based on the assumption that there is a global-level organization in the network. However, in many scenarios, each node is the bearer of complex information and cannot be classified in disjoint clusters. The top-down global view of the partition approach is not designed for this. Here, we represent this complex information as multiple latent labels, and we postulate that edges in the networks are created among nodes carrying similar labels. The latent labels are the communities a node belongs to and we discover them with a simple local-first approach to community discovery. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, its ego neighborhood, using a label propagation algorithm, assuming that each node is aware of the label it shares with each of its connections. The local communities are merged hierarchically, unveiling the modular organization of the network at the global level and identifying overlapping groups and groups of groups. We tested this intuition against the state-of-the-art overlapping community discovery and found that our new method advances in the chosen scenarios in the quality of the obtained communities. We perform a test on benchmark and on real-world networks, evaluating the quality of the community coverage by using the extracted communities to predict the metadata attached to the nodes, which we consider external information about the latent labels. We also provide an explanation about why real-world networks contain overlapping communities and how our logic is able to capture them. Finally, we show how our method is deterministic, is incremental, and has a limited time complexity, so that it can be used on real-world scale networks.1 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/262951102456nas a2200205   4500008003900000020002200039245014900061210006900210260002300279520172700302100002202029700002302051700002102074700001902095700001702114700002002131700001902151700002302170856005702193       2014                          d  a978-88-8467-874-400aUse of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach0 aUse of mobile phone data to estimate mobility flows Measuring ur  aCagliari c06/20143 aThe Big Data, originating from the digital breadcrumbs of human activi-
ties, sensed as a by-product of the technologies that we use for our daily activities, let
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data “proxies”, as the mobile
calls data for mobility. In this paper we investigate to what extent such ”big data”,
in integration with administrative ones, could be a support in producing reliable and
timely estimates of inter-city mobility. The study has been jointly developed by Is-
tat, CNR, University of Pisa in the range of interest of the “Commssione di studio
avente il compito di orientare le scelte dellIstat sul tema dei Big Data ”. In an on-
going project at ISTAT, called “Persons and Places” – based on an integration of
administrative data sources, it has been produced a first release of Origin Destina-
tion matrix – at municipality level – assuming that the places of residence and that
of work (or study) be the terminal points of usual individual mobility for work or
study. The coincidence between the city of residence and that of work (or study) –
is considered as a proxy of the absence of intercity mobility for a person (we define
him a static resident). The opposite case is considered as a proxy of presence of mo-
bility (the person is a dynamic resident: commuter or embedded). As administrative
data do not contain information on frequency of the mobility, the idea is to specify
an estimate method, using calling data as support, to define for each municipality the
stock of standing residents, embedded city users and daily city users (commuters)1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aGiannotti, Fosca1 aMilli, Letizia1 aNanni, Mirco1 aPedreschi, Dino1 aVivio, Roberta1 aGarofalo, Giuseppe  uhttp://www.sis2014.it/proceedings/allpapers/3026.pdf01202nas a2200145   4500008003900000245006500039210006500104520069100169100002300860700002200883700002100905700001700926700001800943856009500961       2014                          d00aUse of mobile phone data to estimate visitors mobility flows0 aUse of mobile phone data to estimate visitors mobility flows3 aBig Data originating from the digital breadcrumbs of human activities,
sensed as by-product of the technologies that we use for our daily activities, allows
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data “proxies”, such as the mo-
bile calls data for mobility. In this paper we investigate to what extent data coming
from mobile operators could be a support in producing reliable and timely estimates
of intra-city mobility flows. The idea is to define an estimation method based on
calling data to characterize the mobility habits of visitors at the level of a single
municipality1 aGabrielli, Lorenzo1 aFurletti, Barbara1 aGiannotti, Fosca1 aNanni, Mirco1 aRinzivillo, S  uhttp://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf00483nas a2200145   4500008004100000245005100041210005100092260000900143100002100152700002000173700001800193700002000211700002100231856008500252       2013                        eng d00aComparing General Mobility and Mobility by Car0 aComparing General Mobility and Mobility by Car  cSept1 aPappalardo, Luca1 aSimini, Filippo1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/comparing-general-mobility-and-mobility-car01933nas a2200169   4500008003900000245004700039210004600086300001200132490000700144520146600151100002501617700002001642700002101662700001901683700002001702856004101722       2013                          d00aEvolving networks: Eras and turning points0 aEvolving networks Eras and turning points  a27–480 v173 aWithin the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network, able to detect the turning points at the beginning of the eras. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks and null models, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset, a collaboration graph extracted from a cinema database, and a network extracted from a database of terrorist attacks; we illustrate how the discovered temporal clustering highlights the crucial moments when the networks witnessed profound changes in their structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttp://dx.doi.org/10.3233/IDA-12056600477nas a2200133   4500008003900000245005700039210005700096100002400153700002000177700001800197700002000215700002100235856008700256       2013                          d00aExplaining the PRoduct Range Effect in Purchase Data0 aExplaining the PRoduct Range Effect in Purchase Data1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/explaining-product-range-effect-purchase-data01161nas a2200145   4500008003900000245004100039210003800080260000900118520071500127100002000842700001900862700002100881700002100902856009200923       2013                          d00aOn multidimensional network measures0 amultidimensional network measures  c20133 aNetworks, i.e., sets of interconnected entities, are ubiquitous,
spanning disciplines as diverse as sociology, biology and computer science.
The recent availability of large amounts of network data has thus
provided a unique opportunity to develop models and analysis tools applicable
to a wide range of scenarios. However, real-world phenomena are
often more complex than existing graph data models. One relevant example
concerns the numerous types of social relationships (or edges) that
can be present between individuals in a social network. In this short paper
we present a unified model and a set of measures recently developed
to represent and analyze network data with multiple types of edges.1 aMagnani, Matteo1 aMonreale, Anna1 aRossetti, Giulio1 aGiannotti, Fosca  uhttps://www.researchgate.net/publication/256194479_On_multidimensional_network_measures01777nas a2200145   4500008003900000245008900039210006900128520121300197100002101410700002201431700001901453700002001472700002101492856011801513       2013                          d00aPrivacy-Preserving Mining of Association Rules From Outsourced Transaction Databases0 aPrivacyPreserving Mining of Association Rules From Outsourced Tr3 aSpurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the corporation (data owner). To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose an attack model based on background knowledge and devise a scheme for privacy preserving outsourced mining. Our scheme ensures that each transformed item is indistinguishable with respect to the attacker's background knowledge, from at least k-1 other transformed items. Our comprehensive experiments on a very large and real transaction database demonstrate that our techniques are effective, scalable, and protect privacy.1 aGiannotti, Fosca1 aLakshmanan, L V S1 aMonreale, Anna1 aPedreschi, Dino1 aWang, Hui, Wendy  uhttps://kdd.isti.cnr.it/publications/privacy-preserving-mining-association-rules-outsourced-transaction-databases02131nas a2200169   4500008003900000245002500039210002500064300001400089520168900103100001901792700001901811700002101830700002101851700002001872700002501892856004401917       2013                          d00aQuantification Trees0 aQuantification Trees  a528–5363 aIn many applications there is a need to monitor how a population is distributed across different classes, and to track the changes in this distribution that derive from varying circumstances, an example such application is monitoring the percentage (or "prevalence") of unemployed people in a given region, or in a given age range, or at different time periods. When the membership of an individual in a class cannot be established deterministically, this monitoring activity requires classification. However, in the above applications the final goal is not determining which class each individual belongs to, but simply estimating the prevalence of each class in the unlabeled data. This task is called quantification. In a supervised learning framework we may estimate the distribution across the classes in a test set from a training set of labeled individuals. However, this may be sub optimal, since the distribution in the test set may be substantially different from that in the training set (a phenomenon called distribution drift). So far, quantification has mostly been addressed by learning a classifier optimized for individual classification and later adjusting the distribution it computes to compensate for its tendency to either under-or over-estimate the prevalence of the class. In this paper we propose instead to use a type of decision trees (quantification trees) optimized not for individual classification, but directly for quantification. Our experiments show that quantification trees are more accurate than existing state-of-the-art quantification methods, while retaining at the same time the simplicity and understandability of the decision tree framework.1 aMilli, Letizia1 aMonreale, Anna1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino1 aSebastiani, Fabrizio  uhttp://dx.doi.org/10.1109/ICDM.2013.12200526nas a2200133   4500008003900000245008000039210006900119490001400188100002000202700001800222700002100240700002000261856011100281       2013                          d00aSpatial and Temporal Evaluation of Network-based Analysis of Human Mobility0 aSpatial and Temporal Evaluation of Networkbased Analysis of Huma0 vto appear1 aCoscia, Michele1 aRinzivillo, S1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/spatial-and-temporal-evaluation-network-based-analysis-human-mobility00453nas a2200145   4500008004100000245004600041210004200087100002400129700002100153700002100174700002000195700002100215700002000236856005100256       2013                        eng d00aThe Three Dimensions of Social Prominence0 aThree Dimensions of Social Prominence1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttp://dx.doi.org/10.1007/978-3-319-03260-3_2801029nas a2200169   4500008004100000245004700041210004500088300001200133490000800145520055800153100002100711700001800732700001400750700002000764700002100784856005400805       2013                        eng d00a{Understanding the patterns of car travel}0 aUnderstanding the patterns of car travel  a61–730 v2153 a{Are the patterns of car travel different from those of general human mobility? Based on a unique dataset consisting of the GPS trajectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illustrate novel analytical findings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real traffic values as measured by road sensors.}1 aPappalardo, Luca1 aRinzivillo, S1 aQu, Zehui1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1140/epjst%252fe2013-01715-500579nas a2200133   4500008003900000245009500039210006900134100002000203700002100223700002400244700002400268700002100292856013200313       2013                          d00aYou Know Because I Know”: a Multidimensional Network Approach to Human Resources Problem0 aYou Know Because I Know a Multidimensional Network Approach to H1 aCoscia, Michele1 aRossetti, Giulio1 aPennacchioli, Diego1 aCeccarelli, Damiano1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/you-know-because-i-know%E2%80%9D-multidimensional-network-approach-human-resources-problem00754nas a2200241   4500008004100000245007800041210006900119100001900188700002100207700001700228700002100245700001900266700001600285700002300301700001700324700001700341700002000358700002000378700002200398700002600420700001600446856005000462       2012                        eng d00aAn Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants0 aAgentBased Model to Evaluate Carpooling at Large Manufacturing P1 aBellemans, Tom1 aBothe, Sebastian1 aCho, Sungjin1 aGiannotti, Fosca1 aJanssens, Davy1 aKnapen, Luk1 aKörner, Christine1 aMay, Michael1 aNanni, Mirco1 aPedreschi, Dino1 aStange, Hendrik1 aTrasarti, Roberto1 aYasar, Ansar-Ul-Haque1 aWets, Geert  uhttp://dx.doi.org/10.1016/j.procs.2012.08.00100515nas a2200157   4500008003900000245004500039210004400084260002100128100002200149700002200171700002300193700001800216700002100234700002100255856008100276       2012                          d00aAnalisi di Mobilita' con dati eterogenei0 aAnalisi di Mobilita con dati eterogenei  aPisabISTI - CNR1 aFurletti, Barbara1 aTrasarti, Roberto1 aGabrielli, Lorenzo1 aRinzivillo, S1 aPappalardo, Luca1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/analisi-di-mobilita-con-dati-eterogenei01748nas a2200169   4500008003900000245008300039210006900122260000900191520116900200100001001369700002101379700001901400700002001419700002101439700001101460856010701471       2012                          d00aAUDIO: An Integrity Auditing Framework of Outlier-Mining-as-a-Service Systems.0 aAUDIO An Integrity Auditing Framework of OutlierMiningasaService  c20123 aSpurred by developments such as cloud computing, there has been considerable recent interest in the data-mining-as-a-service paradigm. Users lacking in expertise or computational resources can outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises issues about result integrity: how can the data owner verify that the mining results returned by the server are correct? In this paper, we present AUDIO, an integrity auditing framework for the specific task of distance-based outlier mining outsourcing. It provides efficient and practical verification approaches to check both completeness and correctness of the mining results. The key idea of our approach is to insert a small amount of artificial tuples into the outsourced data; the artificial tuples will produce artificial outliers and non-outliers that do not exist in the original dataset. The server’s answer is verified by analyzing the presence of artificial outliers/non-outliers, obtaining a probabilistic guarantee of correctness and completeness of the mining result. Our empirical results show the effectiveness and efficiency of our method.1 aR.Liu1 aWang, Hui, Wendy1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, W  uhttps://kdd.isti.cnr.it/publications/audio-integrity-auditing-framework-outlier-mining-service-systems01793nas a2200145   4500008003900000245007100039210006900110300001400179520131900193100001901512700002001531700001901551700002101570856005601591       2012                          d00aClassifying Trust/Distrust Relationships in Online Social Networks0 aClassifying TrustDistrust Relationships in Online Social Network  a552–5573 aOnline social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social balance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slash dot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails.1 aBachi, Giacomo1 aCoscia, Michele1 aMonreale, Anna1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/SocialCom-PASSAT.2012.11500477nas a2200133   4500008003900000245006100039210006100100100001900161700002100180700001700201700002000218700001800238856008700256       2012                          d00aData Science for Simulating the Era of Electric Vehicles0 aData Science for Simulating the Era of Electric Vehicles1 aJanssens, Davy1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/data-science-simulating-era-electric-vehicles00502nas a2200133   4500008003900000245007000039210006800109260000900177100002000186700002100206700002100227700002000248856010000268       2012                          d00aDEMON: a Local-First Discovery Method for Overlapping Communities0 aDEMON a LocalFirst Discovery Method for Overlapping Communities  c20121 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/demon-local-first-discovery-method-overlapping-communities00430nas a2200121   4500008004100000245007000041210006800111100002000179700002100199700002100220700002000241856004700261       2012                        eng d00aDEMON: a local-first discovery method for overlapping communities0 aDEMON a localfirst discovery method for overlapping communities1 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/2339530.233963001549nas a2200169   4500008003900000022001400039245005900053210005900112520102300171100001801194700002101212700001901233700002001252700002101272700002001293856006601313       2012                          d  a0933-187500aDiscovering the Geographical Borders of Human Mobility0 aDiscovering the Geographical Borders of Human Mobility3 aThe availability of massive network and mobility data from diverse domains has fostered the analysis of human behavior and interactions. Broad, extensive, and multidisciplinary research has been devoted to the extraction of non-trivial knowledge from this novel form of data. We propose a general method to determine the influence of social and mobility behavior over a specific geographical area in order to evaluate to what extent the current administrative borders represent the real basin of human movement. We build a network representation of human movement starting with vehicle GPS tracks and extract relevant clusters, which are then mapped back onto the territory, finding a good match with the existing administrative borders. The novelty of our approach is the focus on a detailed spatial resolution, we map emerging borders in terms of individual municipalities, rather than macro regional or national areas. We present a series of experiments to illustrate and evaluate the effectiveness of our approach.1 aRinzivillo, S1 aMainardi, Simone1 aPezzoni, Fabio1 aCoscia, Michele1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007%2Fs13218-012-0181-800475nas a2200121   4500008004100000245007700041210006900118100002200187700001900209700001700228700002100245856008700266       2012                        eng d00aIndividual Mobility Profiles: Methods and Application on Vehicle Sharing0 aIndividual Mobility Profiles Methods and Application on Vehicle 1 aTrasarti, Roberto1 aPinelli, Fabio1 aNanni, Mirco1 aGiannotti, Fosca  uhttp://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb01411nas a2200157   4500008003900000245007400039210006900113300001400182520091000196100001701106700001901123700002001142700002601162700002101188856004401209       2012                          d00aInjecting Discrimination and Privacy Awareness Into Pattern Discovery0 aInjecting Discrimination and Privacy Awareness Into Pattern Disc  a360–3693 aData mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. Data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a combined pattern sanitization framework that yields both privacy and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion.1 aHajian, Sara1 aMonreale, Anna1 aPedreschi, Dino1 aDomingo-Ferrer, Josep1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/ICDMW.2012.5102055nas a2200169   4500008003900000245006600039210006500105260001200170490002200182520150900204100002501713700002001738700002101758700001901779700002001798856006701818       2012                          d00aMultidimensional networks: foundations of structural analysis0 aMultidimensional networks foundations of structural analysis  c10/20120 v Volume 15 / 20123 aComplex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. So far, network analysis has focused on the characterization and measurement of local and global properties of graphs, such as diameter, degree distribution, centrality, and so on. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens in monodimensional networks, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. We tested our framework on different real world multidimensional networks, showing the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks. 1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttp://www.springerlink.com/content/f774289854430410/abstract/00498nas a2200133   4500008003900000245006600039210006600105260002200171100002000193700001800213700002000231700002100251856009200272       2012                          d00aOptimal Spatial Resolution for the Analysis of Human Mobility0 aOptimal Spatial Resolution for the Analysis of Human Mobility  aInstanbul, Turkey1 aCoscia, Michele1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/optimal-spatial-resolution-analysis-human-mobility02994nas a2200205   4500008004100000245003100041210003100072300000800103490000800111520246000119100001902579700002102598700002102619700002402640700002102664700002202685700002302707700002102730856003702751       2012                        eng d00aSmart cities of the future0 aSmart cities of the future  a4810 v2143 aHere we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for urban populations. We begin by defining the state of the art, explaining the science of smart cities. We define six scenarios based on new cities badging themselves as smart, older cities regenerating themselves as smart, the development of science parks, tech cities, and technopoles focused on high technologies, the development of urban services using contemporary ICT, the use of ICT to develop new urban intelligence functions, and the development of online and mobile forms of participation. Seven project areas are then proposed: Integrated Databases for the Smart City, Sensing, Networking and the Impact of New Social Media, Modelling Network Performance, Mobility and Travel Behaviour, Modelling Urban Land Use, Transport and Economic Interactions, Modelling Urban Transactional Activities in Labour and Housing Markets, Decision Support as Urban Intelligence, Participatory Governance and Planning Structures for the Smart City. Finally we anticipate the paradigm shifts that will occur in this research and define a series of key demonstrators which we believe are important to progressing a science of smart cities.1 aBatty, Michael1 aAxhausen, Kay, W1 aGiannotti, Fosca1 aPozdnoukhov, Alexei1 aBazzani, Armando1 aWachowicz, Monica1 aOuzounis, Georgios1 aPortugali, Yuval  uhttps://kdd.isti.cnr.it/node/60100495nas a2200133   4500008003900000245007300039210006900112300001200181490000600193100002000199700002100219700002000240856010100260       2011                          d00aA classification for community discovery methods in complex networks0 aclassification for community discovery methods in complex networ  a512-5460 v41 aCoscia, Michele1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/classification-community-discovery-methods-complex-networks00486nas a2200121   4500008003900000245007200039210006900111300001200180100002500192700002000217700002100237856010600258       2011                          d00aFinding and Characterizing Communities in Multidimensional Networks0 aFinding and Characterizing Communities in Multidimensional Netwo  a490-4941 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-and-characterizing-communities-multidimensional-networks00506nas a2200121   4500008003900000245008100039210006900120300001400189100002500203700002000228700002100248856011500269       2011                          d00aFinding redundant and complementary communities in multidimensional networks0 aFinding redundant and complementary communities in multidimensio  a2181-21841 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-redundant-and-complementary-communities-multidimensional-networks01804nas a2200157   4500008003900000245005300039210005300092300001200145520129700157100002501454700002001479700002101499700001901520700002001539856008701559       2011                          d00aFoundations of Multidimensional Network Analysis0 aFoundations of Multidimensional Network Analysis  a485-4893 aComplex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens inmonodimensional network, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we develop a solid repertoire of basic concepts and analytical measures, which takes into account the general structure of multidimensional networks. We tested our framework on a real world multidimensional network, showing the validity and the meaningfulness of the measures introduced, that are able to extract important, nonrandom, information about complex phenomena.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/foundations-multidimensional-network-analysis00347nas a2200109   4500008004100000245004600041210004600087100002100133700002500154700002100179856003700200       2011                        eng d00aLink Prediction su Reti Multidimensionali0 aLink Prediction su Reti Multidimensionali1 aRossetti, Giulio1 aBerlingerio, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/node/62700449nas a2200133   4500008003900000245005000039210005000089300001400139100002200153700001900175700001700194700002100211856008300232       2011                          d00aMining mobility user profiles for car pooling0 aMining mobility user profiles for car pooling  a1190-11981 aTrasarti, Roberto1 aPinelli, Fabio1 aNanni, Mirco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-mobility-user-profiles-car-pooling01493nas a2200157   4500008003900000245006200039210006000101260000900161520096900170100002101139700002201160700001901182700002001201700002101221856009301242       2011                          d00aPrivacy-preserving data mining from outsourced databases.0 aPrivacypreserving data mining from outsourced databases  c20113 aSpurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service: a company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the outsourced database and the knowledge extract from it by data mining are considered private property of the data owner. To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing a data mining task within a corporate privacy-preserving framework. We propose a scheme for privacy-preserving outsourced mining which offers a formal protection against information disclosure, and show that the data owner can recover the correct data mining results efficiently.1 aGiannotti, Fosca1 aLakshmanan, L V S1 aMonreale, Anna1 aPedreschi, Dino1 aWang, Hui, Wendy  uhttps://kdd.isti.cnr.it/publications/privacy-preserving-data-mining-outsourced-databases01999nas a2200169   4500008003900000245008200039210006900121300001200190490000600202520141100208100002501619700002001644700002101664700001901685700002001704856010501724       2011                          d00aThe pursuit of hubbiness: Analysis of hubs in large multidimensional networks0 apursuit of hubbiness Analysis of hubs in large multidimensional   a223-2370 v23 aHubs are highly connected nodes within a network. In complex network analysis, hubs have been widely studied, and are at the basis of many tasks, such as web search and epidemic outbreak detection. In reality, networks are often multidimensional, i.e., there can exist multiple connections between any pair of nodes. In this setting, the concept of hub depends on the multiple dimensions of the network, whose interplay becomes crucial for the connectedness of a node. In this paper, we characterize multidimensional hubs. We consider the multidimensional generalization of the degree and introduce a new class of measures, that we call Dimension Relevance, aimed at analyzing the importance of different dimensions for the hubbiness of a node. We assess the meaningfulness of our measures by comparing them on real networks and null models, then we study the interplay among dimensions and their effect on node connectivity. Our findings show that: (i) multidimensional hubs do exist and their characterization yields interesting insights and (ii) it is possible to detect the most influential dimensions that cause the different hub behaviors. We demonstrate the usefulness of multidimensional analysis in three real world domains: detection of ambiguous query terms in a word–word query log network, outlier detection in a social network, and temporal analysis of behaviors in a co-authorship network.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/pursuit-hubbiness-analysis-hubs-large-multidimensional-networks00478nas a2200157   4500008003900000245004600039210004400085300001000129490000600139100002200145700002100167700001700188700002000205700001800225856007700243       2011                          d00aA Query Language for Mobility Data Mining0 aQuery Language for Mobility Data Mining  a24-450 v71 aTrasarti, Roberto1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/query-language-mobility-data-mining00474nas a2200133   4500008003900000245005800039210005800097260001400155300001200169100002100181700002500202700002100227856009200248       2011                          d00aScalable Link Prediction on Multidimensional Networks0 aScalable Link Prediction on Multidimensional Networks  aVancouver  a979-9861 aRossetti, Giulio1 aBerlingerio, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/scalable-link-prediction-multidimensional-networks00528nas a2200169   4500008003900000245004600039210004600085300001200131100001700143700001900160700002200179700001700201700001800218700001800236700002100254856008300275       2011                          d00aTraffic Jams Detection Using Flock Mining0 aTraffic Jams Detection Using Flock Mining  a650-6531 aOng, Rebecca1 aPinelli, Fabio1 aTrasarti, Roberto1 aNanni, Mirco1 aRenso, Chiara1 aRinzivillo, S1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/traffic-jams-detection-using-flock-mining00659nas a2200181   4500008003900000245009400039210006900133300001200202490000700214100002100221700001700242700002000259700001900279700001800298700001800316700002200334856012100356       2011                          d00aUnveiling the complexity of human mobility by querying and mining massive trajectory data0 aUnveiling the complexity of human mobility by querying and minin  a695-7190 v201 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aPinelli, Fabio1 aRenso, Chiara1 aRinzivillo, S1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/unveiling-complexity-human-mobility-querying-and-mining-massive-trajectory-data00539nas a2200145   4500008003900000245007400039210006900113300001200182100001700194700002200211700001800233700002100251700002000272856010100292       2010                          d00aAdvanced knowledge discovery on movement data with the GeoPKDD system0 aAdvanced knowledge discovery on movement data with the GeoPKDD s  a693-6961 aNanni, Mirco1 aTrasarti, Roberto1 aRenso, Chiara1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/advanced-knowledge-discovery-movement-data-geopkdd-system-000537nas a2200145   4500008003900000245007400039210006900113300001200182100001700194700002200211700001800233700002100251700002000272856009900292       2010                          d00aAdvanced knowledge discovery on movement data with the GeoPKDD system0 aAdvanced knowledge discovery on movement data with the GeoPKDD s  a693-6961 aNanni, Mirco1 aTrasarti, Roberto1 aRenso, Chiara1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/advanced-knowledge-discovery-movement-data-geopkdd-system01803nas a2200157   4500008003900000245006600039210006500105300001000170520126700180100002501447700002001472700002101492700001901513700002001532856009301552       2010                          d00aAs Time Goes by: Discovering Eras in Evolving Social Networks0 aAs Time Goes by Discovering Eras in Evolving Social Networks  a81-903 aWithin the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus instead on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset; we illustrate how the discovered temporal clustering highlights the crucial moments when the network had profound changes in its structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/time-goes-discovering-eras-evolving-social-networks00537nas a2200145   4500008003900000245006900039210006700108300001000175100002500185700002000210700002100230700001900251700002000270856010100290       2010                          d00aDiscovering Eras in Evolving Social Networks (Extended Abstract)0 aDiscovering Eras in Evolving Social Networks Extended Abstract  a78-851 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/discovering-eras-evolving-social-networks-extended-abstract01089nas a2200193   4500008003900000245004600039210004500085300001200130520052100142100002200663700001800685700001900703700001700722700001900739700001800758700002000776700002100796856007800817       2010                          d00aExploring Real Mobility Data with M-Atlas0 aExploring Real Mobility Data with MAtlas  a624-6273 aResearch on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applications.1 aTrasarti, Roberto1 aRinzivillo, S1 aPinelli, Fabio1 aNanni, Mirco1 aMonreale, Anna1 aRenso, Chiara1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/exploring-real-mobility-data-m-atlas01250nas a2200169   4500008003900000022002200039245006500061210006200126520069900188100002300887700002300910700002100933700001900954700002000973700001800993856006901011       2010                          d  a978-989-20-1953-600aA Generalisation-based Approach to Anonymising Movement Data0 aGeneralisationbased Approach to Anonymising Movement Data3 aThe possibility to collect, store, disseminate, and analyze data about movements of people raises
very serious privacy concerns, given the sensitivity of the information about personal positions. In
particular, sensitive information about individuals can be uncovered with the use of data mining and
visual analytics methods. In this paper we present a method for the generalization of trajectory data
that can be adopted as the first step of a process to obtain k-anonymity in spatio-temporal datasets.
We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this
method of generalization of trajectories preserves the clustering analysis results. 1 aAndrienko, Gennady1 aAndrienko, Natalia1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino1 aRinzivillo, S  uhttp://agile2010.dsi.uminho.pt/pen/ShortPapers_PDF%5C122_DOC.pdf00526nas a2200133   4500008003900000245007800039210006900117300001200186100001900198700001900217700002200236700002100258856011300279       2010                          d00aLocation Prediction through Trajectory Pattern Mining (Extended Abstract)0 aLocation Prediction through Trajectory Pattern Mining Extended A  a134-1411 aMonreale, Anna1 aPinelli, Fabio1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/location-prediction-through-trajectory-pattern-mining-extended-abstract00607nas a2200169   4500008003900000245007700039210006900116300000900185100002100194700001700215700002000232700001900252700001800271700001800289700002200307856010800329       2010                          d00aMobility data mining: discovering movement patterns from trajectory data0 aMobility data mining discovering movement patterns from trajecto  a7-101 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aPinelli, Fabio1 aRenso, Chiara1 aRinzivillo, S1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/mobility-data-mining-discovering-movement-patterns-trajectory-data02124nas a2200193   4500008003900000245005100039210005100090300001300141490000600154520158100160100001901741700002301760700002301783700002101806700002001827700001801847700001901865856004601884       2010                          d00aMovement Data Anonymity through Generalization0 aMovement Data Anonymity through Generalization  a91–1210 v33 aWireless networks and mobile devices, such as mobile phones and GPS receivers, sense
and track the movements of people and vehicles, producing society-wide mobility databases. This is
a challenging scenario for data analysis and mining. On the one hand, exciting opportunities arise out
of discovering new knowledge about human mobile behavior, and thus fuel intelligent info-mobility
applications. On other hand, new privacy concerns arise when mobility data are published. The
risk is particularly high for GPS trajectories, which represent movement of a very high precision and
spatio-temporal resolution: the de-identification of such trajectories (i.e., forgetting the ID of their
associated owners) is only a weak protection, as generally it is possible to re-identify a person by observing
her routine movements. In this paper we propose a method for achieving true anonymity in
a dataset of published trajectories, by defining a transformation of the original GPS trajectories based
on spatial generalization and k-anonymity. The proposed method offers a formal data protection
safeguard, quantified as a theoretical upper bound to the probability of re-identification. We conduct
a thorough study on a real-life GPS trajectory dataset, and provide strong empirical evidence that
the proposed anonymity techniques achieve the conflicting goals of data utility and data privacy. In
practice, the achieved anonymity protection is much stronger than the theoretical worst case, while
the quality of the cluster analysis on the trajectory data is preserved.1 aMonreale, Anna1 aAndrienko, Gennady1 aAndrienko, Natalia1 aGiannotti, Fosca1 aPedreschi, Dino1 aRinzivillo, S1 aWrobel, Stefan  uhttp://www.tdp.cat/issues/abs.a045a10.php01363nas a2200145   4500008003900000245004900039210004900088520089500137100002501032700002001057700002101077700001901098700002001117856008001137       2010                          d00aTowards Discovery of Eras in Social Networks0 aTowards Discovery of Eras in Social Networks3 aIn the last decades, much research has been devoted in topics related to Social Network Analysis. One important direction in this area is to analyze the temporal evolution of a network. So far, previous approaches analyzed this setting at both the global and the local level. In this paper, we focus on finding a way to detect temporal eras in an evolving network. We pose the basis for a general framework that aims at helping the analyst in browsing the temporal clusters both in a top-down and bottom-up way, exploring the network at any level of temporal details. We show the effectiveness of our approach on real data, by applying our proposed methodology to a co-authorship network extracted from a bibliographic dataset. Our first results are encouraging, and open the way for the definition and implementation of a general framework for discovering eras in evolving social networks.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/towards-discovery-eras-social-networks00602nas a2200169   4500008003900000245007300039210006900112300000900181490000700190100002200197700002100219700002200240700002300262700002100285700002200306856010400328       2009                          d00aA constraint-based querying system for exploratory pattern discovery0 aconstraintbased querying system for exploratory pattern discover  a3-270 v341 aBonchi, Francesco1 aGiannotti, Fosca1 aLucchese, Claudio1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/constraint-based-querying-system-exploratory-pattern-discovery00597nas a2200169   4500008003900000245007300039210006900112300000900181490000700190100002200197700002100219700002200240700002300262700002100285700002200306856009900328       2009                          d00aA constraint-based querying system for exploratory pattern discovery0 aconstraintbased querying system for exploratory pattern discover  a3-270 v341 aBonchi, Francesco1 aGiannotti, Fosca1 aLucchese, Claudio1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/constraint-based-querying-system-exploratory-pattern-discovery00457nas a2200121   4500008003900000245006200039210006100101300001400162100002100176700002000197700002400217856009400241       2009                          d00aGeographic privacy-aware knowledge discovery and delivery0 aGeographic privacyaware knowledge discovery and delivery  a1157-11581 aGiannotti, Fosca1 aPedreschi, Dino1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/geographic-privacy-aware-knowledge-discovery-and-delivery00517nas a2200145   4500008003900000245006100039210005600100100002100156700001700177700002000194700001800214700001800232700002200250856009900272       2009                          d00aGeoPKDD – Geographic Privacy-aware Knowledge Discovery0 aGeoPKDD Geographic Privacyaware Knowledge Discovery1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRinzivillo, S1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/geopkdd-%E2%80%93-geographic-privacy-aware-knowledge-discovery00573nas a2200145   4500008003900000245008400039210006900123300001200192100002500204700002200229700002000251700002100271700001900292856011600311       2009                          d00aMining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation0 aMining Clinical Immunological and Genetic Data of Solid Organ Tr  a211-2361 aBerlingerio, Michele1 aBonchi, Francesco1 aCurcio, Michele1 aGiannotti, Fosca1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/mining-clinical-immunological-and-genetic-data-solid-organ-transplantation00477nas a2200145   4500008003900000245005000039210005000089300001200139100002100151700001700172700002000189700001800209700002200227856008200249       2009                          d00aMining Mobility Behavior from Trajectory Data0 aMining Mobility Behavior from Trajectory Data  a948-9511 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/mining-mobility-behavior-trajectory-data00423nas a2200121   4500008003900000245005200039210005200091300001200143100002500155700002000180700002100200856008000221       2009                          d00aMining the Information Propagation in a Network0 aMining the Information Propagation in a Network  a333-3401 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-information-propagation-network00425nas a2200121   4500008003900000245005200039210005200091300001200143100002500155700002000180700002100200856008200221       2009                          d00aMining the Information Propagation in a Network0 aMining the Information Propagation in a Network  a333-3401 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-information-propagation-network-000460nas a2200121   4500008003900000245006500039210006500104300001200169100002500181700002000206700002100226856009100247       2009                          d00aMining the Temporal Dimension of the Information Propagation0 aMining the Temporal Dimension of the Information Propagation  a237-2481 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-temporal-dimension-information-propagation00455nas a2200121   4500008003900000245006500039210006500104300001200169100002500181700002000206700002100226856008600247       2009                          d00aMining the Temporal Dimension of the Information Propagation0 aMining the Temporal Dimension of the Information Propagation  a237-2481 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/mining-temporal-dimension-information-propagation01604nas a2200157   4500008004100000245005100041210005100092260000800143520109900151100002301250700002301273700002101296700001901317700002001336856009001356       2009                        eng d00aMovement data anonymity through generalization0 aMovement data anonymity through generalization  bACM3 aIn recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the diffusion of mobile devices (e.g., mobile phones, RFID devices and GPS devices) and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Clearly, in these applications privacy is a concern, since models extracted from this kind of data can reveal the behavior of group of individuals, thus compromising their privacy. Movement data present a new challenge for the privacy-preserving data mining community because of their spatial and temporal characteristics.

In this position paper we briefly present an approach for the generalization of movement data that can be adopted for obtaining k-anonymity in spatio-temporal datasets; specifically, it can be used to realize a framework for publishing of spatio-temporal data while preserving privacy. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.1 aAndrienko, Gennady1 aAndrienko, Natalia1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/movement-data-anonymity-through-generalization-000527nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012500280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography00529nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012700280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography-000472nas a2200133   4500008003900000245005900039210005900098300001200157100002500169700001900194700001700213700002100230856008700251       2009                          d00aTemporal mining for interactive workflow data analysis0 aTemporal mining for interactive workflow data analysis  a109-1181 aBerlingerio, Michele1 aPinelli, Fabio1 aNanni, Mirco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/temporal-mining-interactive-workflow-data-analysis00486nas a2200145   4500008003900000245005000039210005000089260003100139300001000170100002100180700001700201700002000218700001900238856008300257       2009                          d00aTrajectory pattern analysis for urban traffic0 aTrajectory pattern analysis for urban traffic  aSEATTLE, USAbACMc11/2009  a43-471 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aPinelli, Fabio  uhttps://kdd.isti.cnr.it/publications/trajectory-pattern-analysis-urban-traffic00572nas a2200157   4500008003900000245006500039210006500104260003200169100002300201700002300224700001800247700001700265700002000282700002100302856009100323       2009                          d00aVisual Cluster Analysis of Large Collections of Trajectories0 aVisual Cluster Analysis of Large Collections of Trajectories  bIEEE Computer Society Press1 aAndrienko, Gennady1 aAndrienko, Natalia1 aRinzivillo, S1 aNanni, Mirco1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/visual-cluster-analysis-large-collections-trajectories02208nas a2200133   4500008003900000245006500039210006400104520173400168100001901902700001901921700002201940700002101962856009101983       2009                          d00aWhereNext: a Location Predictor on Trajectory Pattern Mining0 aWhereNext a Location Predictor on Trajectory Pattern Mining3 aThe pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.1 aMonreale, Anna1 aPinelli, Fabio1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/wherenext-location-predictor-trajectory-pattern-mining00451nas a2200145   4500008004100000245004300041210004300084300001200127490000700139100002100146700002200167700002100189700002000210856007500230       2008                        eng d00aAnonymity preserving pattern discovery0 aAnonymity preserving pattern discovery  a703-7270 v171 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/anonymity-preserving-pattern-discovery00698nas a2200205   4500008004100000245008100041210006900122300001000191490000700201100002600208700001800234700001500252700002100267700001500288700002200303700001800325700002600343700001900369856010400388       2008                        eng d00aAn Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology0 aApplication of Advanced SpatioTemporal Formalisms to Behavioural  a37-720 v121 aRaffaetà, Alessandra1 aCeccarelli, T1 aCenteno, D1 aGiannotti, Fosca1 aMassolo, A1 aParent, Christine1 aRenso, Chiara1 aSpaccapietra, Stefano1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/application-advanced-spatio-temporal-formalisms-behavioural-ecology00659nas a2200181   4500008004100000245008100041210006900122100001800191700001500209700002100224700001500245700002200260700002600282700001800308700002600326700001900352856010600371       2008                        eng d00aAn Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology0 aApplication of Advanced SpatioTemporal Formalisms to Behavioural1 aCeccarelli, T1 aCenteno, D1 aGiannotti, Fosca1 aMassolo, A1 aParent, Christine1 aRaffaetà, Alessandra1 aRenso, Chiara1 aSpaccapietra, Stefano1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/application-advanced-spatio-temporal-formalisms-behavioural-ecology-000574nas a2200145   4500008003900000245009800039210006900137300000700206100001800213700002300231700002100254700001900275700001700294856011700311       2008                          d00aClustering of German municipalities based on mobility characteristics: an overview of results0 aClustering of German municipalities based on mobility characteri  a691 aZanda, Andrea1 aKörner, Christine1 aGiannotti, Fosca1 aSchulz, Daniel1 aMay, Michael  uhttps://kdd.isti.cnr.it/content/clustering-german-municipalities-based-mobility-characteristics-overview-results00662nas a2200193   4500008004100000245007300041210006900114300001200183100002100195700001500216700001600231700002200247700001800269700002100287700002000308700001800328700002400346856009800370       2008                        eng d00aDAEDALUS: A knowledge discovery analysis framework for movement data0 aDAEDALUS A knowledge discovery analysis framework for movement d  a191-1981 aOrtale, Riccardo1 aRitacco, E1 aPelekisy, N1 aTrasarti, Roberto1 aCosta, Gianni1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/daedalus-knowledge-discovery-analysis-framework-movement-data00665nas a2200193   4500008003900000245007700039210006900116300000700185100002100192700001500213700001900228700002200247700001800269700002100287700002000308700001800328700002400346856010100370       2008                          d00aThe DAEDALUS framework: progressive querying and mining of movement data0 aDAEDALUS framework progressive querying and mining of movement d  a521 aOrtale, Riccardo1 aRitacco, E1 aPelekis, Nikos1 aTrasarti, Roberto1 aCosta, Gianni1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/daedalus-framework-progressive-querying-and-mining-movement-data01916nas a2200145   4500008003900000245007900039210006900118260002000187520137500207100001901582700001901601700002201620700002101642856010701663       2008                          d00aLocation prediction within the mobility data analysis environment Daedalus0 aLocation prediction within the mobility data analysis environmen  aDublin, Ireland3 aIn this paper we propose a method to predict the next location of a moving object based on two recent results in GeoPKDD project: DAEDALUS, a mobility data analysis environment and Trajectory Pattern, a sequential pattern mining algorithm with temporal annotation integrated in DAEDALUS. The first one is a DMQL environment for moving objects, where both data and patterns can be represented. The second one extracts movement patterns as sequences of movements between locations with typical travel times.

This paper proposes a prediction method which uses the local models extracted by Trajectory Pattern to build a global model called Prediction Tree. The future location of a moving object is predicted visiting the tree and calculating the best matching function.

The integration within DAEDALUS system supports an interactive construction of the predictor on the top of a set of spatio-temporal patterns.

Others proposals in literature base the definition of prediction methods for future location of a moving object on previously extracted frequent patterns. They use the recent history of movements of the object itself and often use time only to order the events. Our work uses the movements of all moving objects in a certain area to learn a classifier built on the mined trajectory patterns, which are intrinsically equipped with temporal information.1 aPinelli, Fabio1 aMonreale, Anna1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/location-prediction-within-mobility-data-analysis-environment-daedalus00411nas a2200109   4500008003900000245006300039210006100102300000900163100002100172700002000193856008800213       2008                          d00aMobility, Data Mining and Privacy: A Vision of Convergence0 aMobility Data Mining and Privacy A Vision of Convergence  a1-111 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/mobility-data-mining-and-privacy-vision-convergence00541nam a2200145   4500008003900000020002200039245007100061210006800132260001300200100002100213700002000234700002100254700002000275856010000295       2008                          d  a978-3-540-75176-200aMobility, Data Mining and Privacy - Geographic Knowledge Discovery0 aMobility Data Mining and Privacy Geographic Knowledge Discovery  bSpringer1 aGiannotti, Fosca1 aPedreschi, Dino1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/mobility-data-mining-and-privacy-geographic-knowledge-discovery00472nas a2200121   4500008003900000245007600039210006900115300001000184100002100194700002000215700001900235856009600254       2008                          d00aMobility, Data Mining and Privacy the Experience of the GeoPKDD Project0 aMobility Data Mining and Privacy the Experience of the GeoPKDD P  a25-321 aGiannotti, Fosca1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/mobility-data-mining-and-privacy-experience-geopkdd-project00595nas a2200157   4500008004100000245005900041210005800100260006700158100002000225700002100245700002100266700001900287700002600306700001800332856008700350       2008                        eng d00aQuerying and Reasoning for Spatio-Temporal Data Mining0 aQuerying and Reasoning for SpatioTemporal Data Mining  aMobility, Privacy, and Geographyba Knowledge Discovery vision1 aManco, Giuseppe1 aBaglioni, Miriam1 aGiannotti, Fosca1 aKuijpers, Bart1 aRaffaetà, Alessandra1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/querying-and-reasoning-spatio-temporal-data-mining00536nas a2200157   4500008003900000245005800039210005800097300001200155100002000167700002100187700002100208700001900229700002600248700001800274856008600292       2008                          d00aQuerying and Reasoning for Spatiotemporal Data Mining0 aQuerying and Reasoning for Spatiotemporal Data Mining  a335-3741 aManco, Giuseppe1 aBaglioni, Miriam1 aGiannotti, Fosca1 aKuijpers, Bart1 aRaffaetà, Alessandra1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/querying-and-reasoning-spatiotemporal-data-mining00450nas a2200133   4500008004100000245005200041210005100093300001200144100002500156700002100181700001700202700001900219856007800238       2008                        eng d00aTemporal analysis of process logs: a case study0 aTemporal analysis of process logs a case study  a430-4371 aBerlingerio, Michele1 aGiannotti, Fosca1 aNanni, Mirco1 aPinelli, Fabio  uhttps://kdd.isti.cnr.it/content/temporal-analysis-process-logs-case-study00627nas a2200181   4500008003900000245007200039210006900111260002700180300001200207490000600219100001800225700002000243700001700263700002100280700002300301700002300324856009800347       2008                          d00aVisually driven analysis of movement data by progressive clustering0 aVisually driven analysis of movement data by progressive cluster  bPalgrave Macmillan Ltd  a225-2390 v71 aRinzivillo, S1 aPedreschi, Dino1 aNanni, Mirco1 aGiannotti, Fosca1 aAndrienko, Natalia1 aAndrienko, Gennady  uhttps://kdd.isti.cnr.it/content/visually-driven-analysis-movement-data-progressive-clustering00422nas a2200133   4500008004100000245004100041210004100082300001200123100001600135700002100151700002200172700002100194856007300215       2007                        eng d00aHiding Sensitive Trajectory Patterns0 aHiding Sensitive Trajectory Patterns  a693-6981 aAbul, Osman1 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/hiding-sensitive-trajectory-patterns00362nas a2200133   4500008004100000245002100041210002100062300001200083100001600095700002100111700002200132700002100154856005300175       2007                        eng d00aHiding Sequences0 aHiding Sequences  a233-2411 aAbul, Osman1 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/hiding-sequences00364nas a2200133   4500008004100000245002100041210002100062300001200083100001600095700002100111700002200132700002100154856005500175       2007                        eng d00aHiding Sequences0 aHiding Sequences  a147-1561 aAbul, Osman1 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/hiding-sequences-000490nas a2200133   4500008004100000245006500041210006400106300001200170100002500182700002200207700002100229700001900250856008700269       2007                        eng d00aMining Clinical Data with a Temporal Dimension: A Case Study0 aMining Clinical Data with a Temporal Dimension A Case Study  a429-4361 aBerlingerio, Michele1 aBonchi, Francesco1 aGiannotti, Fosca1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/mining-clinical-data-temporal-dimension-case-study00496nas a2200145   4500008004100000245005700041210005600098300001200154100002100166700002200187700002100209700002000230700001600250856008400266       2007                        eng d00aPrivacy-Aware Knowledge Discovery from Location Data0 aPrivacyAware Knowledge Discovery from Location Data  a283-2871 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino1 aAbul, Osman  uhttps://kdd.isti.cnr.it/content/privacy-aware-knowledge-discovery-location-data00460nas a2200133   4500008004100000245005300041210005200094300001200146100002500158700002200183700002100205700001900226856008100245       2007                        eng d00aTime-Annotated Sequences for Medical Data Mining0 aTimeAnnotated Sequences for Medical Data Mining  a133-1381 aBerlingerio, Michele1 aBonchi, Francesco1 aGiannotti, Fosca1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/time-annotated-sequences-medical-data-mining00409nas a2200121   4500008004100000245004500041210004400086300001200130100002500142700002200167700002100189856007700210       2007                        eng d00aTowards Constraint-Based Subgraph Mining0 aTowards ConstraintBased Subgraph Mining  a274-2811 aBerlingerio, Michele1 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/towards-constraint-based-subgraph-mining00386nas a2200133   4500008004100000245003000041210003000071300001200101100002100113700001700134700001900151700002000170856006200190       2007                        eng d00aTrajectory pattern mining0 aTrajectory pattern mining  a330-3391 aGiannotti, Fosca1 aNanni, Mirco1 aPinelli, Fabio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/trajectory-pattern-mining00598nas a2200157   4500008004100000245008300041210006900124300000800193100002200201700002100223700002200244700002300266700002100289700002200310856010800332       2006                        eng d00aConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery0 aConQueSt a Constraintbased Querying System for Exploratory Patte  a1591 aBonchi, Francesco1 aGiannotti, Fosca1 aLucchese, Claudio1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/conquest-constraint-based-querying-system-exploratory-pattern-discovery00403nas a2200109   4500008004100000245005500041210005500096100002100151700001700172700002000189856008400209       2006                        eng d00aEfficient Mining of Temporally Annotated Sequences0 aEfficient Mining of Temporally Annotated Sequences1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/efficient-mining-temporally-annotated-sequences00543nas a2200157   4500008004100000245006000041210005700101300001000158100002200168700002100190700002200211700002300233700002100256700002200277856008600299       2006                        eng d00aOn Interactive Pattern Mining from Relational Databases0 aInteractive Pattern Mining from Relational Databases  a42-621 aBonchi, Francesco1 aGiannotti, Fosca1 aLucchese, Claudio1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/interactive-pattern-mining-relational-databases-000543nas a2200157   4500008004100000245006000041210005700101300001200158100002200170700002200192700002100214700002300235700002100258700002200279856008400301       2006                        eng d00aOn Interactive Pattern Mining from Relational Databases0 aInteractive Pattern Mining from Relational Databases  a329-3381 aLucchese, Claudio1 aBonchi, Francesco1 aGiannotti, Fosca1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/interactive-pattern-mining-relational-databases00524nas a2200157   4500008004100000245005000041210005000091300001200141100002500153700002200178700002100200700002000221700002100241700002200262856008200284       2006                        eng d00aMining HLA Patterns Explaining Liver Diseases0 aMining HLA Patterns Explaining Liver Diseases  a702-7071 aBerlingerio, Michele1 aBonchi, Francesco1 aChelazzi, Silvia1 aCurcio, Michele1 aGiannotti, Fosca1 aScatena, Fabrizio  uhttps://kdd.isti.cnr.it/content/mining-hla-patterns-explaining-liver-diseases00432nas a2200133   4500008004100000245004700041210004700088300001200135100002100147700001700168700002000185700001900205856007400224       2006                        eng d00aMining sequences with temporal annotations0 aMining sequences with temporal annotations  a593-5971 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aPinelli, Fabio  uhttps://kdd.isti.cnr.it/content/mining-sequences-temporal-annotations00506nas a2200133   4500008004100000245006800041210006700109300001200176100002100188700002200209700002100231700002000252856010000272       2006                        eng d00aTowards low-perturbation anonymity preserving pattern discovery0 aTowards lowperturbation anonymity preserving pattern discovery  a588-5921 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/towards-low-perturbation-anonymity-preserving-pattern-discovery00388nas a2200133   4500008004100000245003000041210003000071490000700101100002100108700002200129700002100151700002000172856006200192       2005                        eng d00aAnonymity and data mining0 aAnonymity and data mining0 v201 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/anonymity-and-data-mining00495nas a2200133   4500008004100000245006500041210006500106300001200171100002100183700002200204700002100226700002000247856009400267       2005                        eng d00aBlocking Anonymity Threats Raised by Frequent Itemset Mining0 aBlocking Anonymity Threats Raised by Frequent Itemset Mining  a561-5641 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/blocking-anonymity-threats-raised-frequent-itemset-mining00545nas a2200145   4500008004100000245008100041210006900122300001200191490000600203100002200209700002100231700002200252700002000274856010500294       2005                        eng d00aEfficient breadth-first mining of frequent pattern with monotone constraints0 aEfficient breadthfirst mining of frequent pattern with monotone   a131-1530 v81 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/efficient-breadth-first-mining-frequent-pattern-monotone-constraints00501nas a2200145   4500008004100000245006300041210006100104300001000165490000700175100002200182700002100204700002200225700002000247856008800267       2005                        eng d00aExante: A Preprocessing Method for Frequent-Pattern Mining0 aExante A Preprocessing Method for FrequentPattern Mining  a25-310 v201 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/exante-preprocessing-method-frequent-pattern-mining00481nas a2200133   4500008004100000245006200041210006200103300001000165100002100175700002000196700002000216700001800236856009300254       2005                        eng d00aSynthetic generation of cellular network positioning data0 aSynthetic generation of cellular network positioning data  a12-201 aGiannotti, Fosca1 aMazzoni, Andrea1 aPuntoni, Simone1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/synthetic-generation-cellular-network-positioning-data-000479nas a2200133   4500008004100000245006200041210006200103300001000165100002100175700002000196700002000216700001800236856009100254       2005                        eng d00aSynthetic generation of cellular network positioning data0 aSynthetic generation of cellular network positioning data  a12-201 aGiannotti, Fosca1 aMazzoni, Andrea1 aPuntoni, Simone1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/synthetic-generation-cellular-network-positioning-data00472nas a2200133   4500008004100000245006100041210006100102300001000163100001700173700002100190700001700211700002000228856009000248       2004                        eng d00aDiscovery of ads web hosts through traffic data analysis0 aDiscovery of ads web hosts through traffic data analysis  a76-811 aBacarella, V1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/discovery-ads-web-hosts-through-traffic-data-analysis00452nas a2200121   4500008004100000245006200041210006200103300001200165100002200177700002100199700002000220856009000240       2004                        eng d00aFrequent Pattern Queries for Flexible Knowledge Discovery0 aFrequent Pattern Queries for Flexible Knowledge Discovery  a250-2611 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/frequent-pattern-queries-flexible-knowledge-discovery00711nas a2200157   4500008004100000020001800041245018500059210006900244260001300313490000900326100003000335700002300365700002100388700002000409856012400429       2004                        eng d  a3-540-23108-000aKnowledge Discovery in Databases: PKDD 2004, 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 20-24, 2004, Proceedings0 aKnowledge Discovery in Databases PKDD 2004 8th European Conferen  bSpringer0 v32021 aBoulicaut, Jean-François1 aEsposito, Floriana1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/knowledge-discovery-databases-pkdd-2004-8th-european-conference-principles-and-practice00658nas a2200157   4500008004100000020001800041245012700059210006900186260001300255490000900268100003000277700002300307700002100330700002000351856012900371       2004                        eng d  a3-540-23105-600aMachine Learning: ECML 2004, 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings0 aMachine Learning ECML 2004 15th European Conference on Machine L  bSpringer0 v32011 aBoulicaut, Jean-François1 aEsposito, Floriana1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/machine-learning-ecml-2004-15th-european-conference-machine-learning-pisa-italy-september-2000379nas a2200109   4500008004100000245004900041210004900090300000900139100002200148700002100170856007800191       2004                        eng d00aPushing Constraints to Detect Local Patterns0 aPushing Constraints to Detect Local Patterns  a1-191 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/pushing-constraints-detect-local-patterns00466nas a2200121   4500008004100000245006900041210006600110300001000176100002200186700002100208700002000229856009500249       2004                        eng d00aA Relational Query Primitive for Constraint-Based Pattern Mining0 aRelational Query Primitive for ConstraintBased Pattern Mining  a14-371 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/relational-query-primitive-constraint-based-pattern-mining00496nas a2200133   4500008004100000245007200041210006900113300001400182490000700196100002100203700002000224700001900244856009900263       2004                        eng d00aSpecifying Mining Algorithms with Iterative User-Defined Aggregates0 aSpecifying Mining Algorithms with Iterative UserDefined Aggregat  a1232-12460 v161 aGiannotti, Fosca1 aManco, Giuseppe1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/specifying-mining-algorithms-iterative-user-defined-aggregates00412nas a2200121   4500008004100000245005100041210005100092300001000143100002100153700002000174700001900194856007700213       2004                        eng d00aTowards a Logic Query Language for Data Mining0 aTowards a Logic Query Language for Data Mining  a76-941 aGiannotti, Fosca1 aManco, Giuseppe1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/towards-logic-query-language-data-mining00476nas a2200133   4500008004100000245005900041210005900100300001000159100002200169700002100191700002200212700002000234856008800254       2003                        eng d00aAdaptive Constraint Pushing in Frequent Pattern Mining0 aAdaptive Constraint Pushing in Frequent Pattern Mining  a47-581 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/adaptive-constraint-pushing-frequent-pattern-mining00533nas a2200133   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aWebCat Automatic Categorization of Web Search Results  a507-5181 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aSamaritani, F  uhttps://kdd.isti.cnr.it/content/webcat-automatic-categorization-web-search-results00497nas a2200121   4500008004100000245008500041210006900126300000800195100002100203700002000224700002000244856011100264       2002                        eng d00aCharacterizing Web User Accesses: A Transactional Approach to Web Log Clustering0 aCharacterizing Web User Accesses A Transactional Approach to Web  a3121 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/characterizing-web-user-accesses-transactional-approach-web-log-clustering00372nas a2200121   4500008004100000245003400041210003400075300001200109100002100121700002000142700002000162856006800182       2002                        eng d00aClustering Transactional Data0 aClustering Transactional Data  a175-1871 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/clustering-transactional-data-000358nas a2200097   4500008004100000245005400041210005300095300000600148100002100154856008500175       2002                        eng d00aInvited talk: Logical Data Mining Query Languages0 aInvited talk Logical Data Mining Query Languages  a11 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/invited-talk-logical-data-mining-query-languages00457nas a2200109   4500008004100000245008300041210006900124300001200193100002100205700002000226856010100246       2002                        eng d00aLDL-M$_{\mbox{ine}}$: Integrating Data Mining with Intelligent Query Answering0 aLDLM mbox ine Integrating Data Mining with Intelligent Query Ans  a517-5201 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/ldl-mmboxine-integrating-data-mining-intelligent-query-answering00370nas a2200121   4500008004100000245003400041210003400075300001200109100002100121700002000142700002000162856006600182       2001                        eng d00aClustering Transactional Data0 aClustering Transactional Data  a163-1761 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/clustering-transactional-data00429nas a2200133   4500008004100000245004300041210004300084300001200127100002100139700002600160700001800186700001900204856007200223       2001                        eng d00aComplex Reasoning on Geographical Data0 aComplex Reasoning on Geographical Data  a331-3381 aGiannotti, Fosca1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/complex-reasoning-geographical-data00431nas a2200133   4500008004100000245004300041210004300084300001200127100002100139700002600160700001800186700001900204856007400223       2001                        eng d00aComplex Reasoning on Geographical Data0 aComplex Reasoning on Geographical Data  a331-3381 aGiannotti, Fosca1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/complex-reasoning-geographical-data-000527nas a2200169   4500008004100000245004400041210004400085300001200129100002200141700002100163700002000184700001800204700001700222700002000239700002400259856007400283       2001                        eng d00aData Mining for Intelligent Web Caching0 aData Mining for Intelligent Web Caching  a599-6031 aBonchi, Francesco1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aNanni, Mirco1 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/data-mining-intelligent-web-caching-000525nas a2200169   4500008004100000245004400041210004400085300001200129100002200141700002100163700002000184700001800204700001700222700002000239700002400259856007200283       2001                        eng d00aData Mining for Intelligent Web Caching0 aData Mining for Intelligent Web Caching  a599-6031 aBonchi, Francesco1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aNanni, Mirco1 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/data-mining-intelligent-web-caching00467nas a2200145   4500008004100000245005100041210005000092300001200142490000700154100002100161700002000182700001700202700002000219856008200239       2001                        eng d00aNondeterministic, Nonmonotonic Logic Databases0 aNondeterministic Nonmonotonic Logic Databases  a813-8230 v131 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/nondeterministic-nonmonotonic-logic-databases00525nas a2200133   4500008004100000245008900041210006900130300001000199490000700209100002100216700002000237700001900257856011500276       2001                        eng d00aSemantics and Expressive Power of Nondeterministic Constructs in Deductive Databases0 aSemantics and Expressive Power of Nondeterministic Constructs in  a15-420 v621 aGiannotti, Fosca1 aPedreschi, Dino1 aZaniolo, Carlo  uhttps://kdd.isti.cnr.it/content/semantics-and-expressive-power-nondeterministic-constructs-deductive-databases00500nas a2200121   4500008004100000245008600041210006900127300001200196100002100208700002000229700001900249856011000268       2001                        eng d00aSpecifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study0 aSpecifying Mining Algorithms with Iterative UserDefined Aggregat  a128-1391 aGiannotti, Fosca1 aManco, Giuseppe1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/specifying-mining-algorithms-iterative-user-defined-aggregates-case-study00650nas a2200193   4500008004100000245006800041210006800109300001200177490000700189100002200196700002100218700002000239700002000259700001700279700002000296700001800316700002400334856009800358       2001                        eng d00aWeb log data warehousing and mining for intelligent web caching0 aWeb log data warehousing and mining for intelligent web caching  a165-1890 v391 aBonchi, Francesco1 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/web-log-data-warehousing-and-mining-intelligent-web-caching-000648nas a2200193   4500008004100000245006800041210006800109300001200177490000700189100002200196700002100218700002000239700002000259700001700279700002000296700001800316700002400334856009600358       2001                        eng d00aWeb log data warehousing and mining for intelligent web caching0 aWeb log data warehousing and mining for intelligent web caching  a165-1890 v391 aBonchi, Francesco1 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/web-log-data-warehousing-and-mining-intelligent-web-caching00607nas a2200169   4500008004100000245006800041210006800109100002200177700002100199700002000220700002000240700001700260700002000277700001800297700002400315856009800339       2001                        eng d00aWeb Log Data Warehousing and Mining for Intelligent Web Caching0 aWeb Log Data Warehousing and Mining for Intelligent Web Caching1 aBonchi, Francesco1 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/web-log-data-warehousing-and-mining-intelligent-web-caching-100456nas a2200109   4500008004100000245007800041210006900119300001200188100002100200700002000221856010500241       2000                        eng d00aDeclarative Knowledge Extraction with Interactive User-Defined Aggregates0 aDeclarative Knowledge Extraction with Interactive UserDefined Ag  a435-4441 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/declarative-knowledge-extraction-interactive-user-defined-aggregates00408nas a2200121   4500008004100000245004900041210004800090300001200138100002100150700001700171700002000188856007800208       2000                        eng d00aLogic-Based Knowledge Discovery in Databases0 aLogicBased Knowledge Discovery in Databases  a279-2831 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/logic-based-knowledge-discovery-databases00395nas a2200109   4500008004100000245005300041210005300094300001200147100002100159700002000180856008500200       2000                        eng d00aMaking Knowledge Extraction and Reasoning Closer0 aMaking Knowledge Extraction and Reasoning Closer  a360-3711 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/making-knowledge-extraction-and-reasoning-closer00405nas a2200121   4500008004100000245004800041210004500089300001200134100002200146700002100168700002000189856007400209       2000                        eng d00aOn Verification in Logic Database Languages0 aVerification in Logic Database Languages  a957-9711 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/verification-logic-database-languages00476nas a2200121   4500008004100000245007200041210006900113300000800182100002100190700002300211700002400234856009600258       1999                        eng d00aBeyond Current Technology: The Perspective of Three EC GIS Projects0 aBeyond Current Technology The Perspective of Three EC GIS Projec  a5101 aGiannotti, Fosca1 aJeansoulin, Robert1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/beyond-current-technology-perspective-three-ec-gis-projects00539nas a2200133   4500008004100000245008800041210006900129300001200198100002200210700002100232700002100253700002000274856011100294       1999                        eng d00aA Classification-Based Methodology for Planning Audit Strategies in Fraud Detection0 aClassificationBased Methodology for Planning Audit Strategies in  a175-1841 aBonchi, Francesco1 aGiannotti, Fosca1 aMainetto, Gianni1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/classification-based-methodology-planning-audit-strategies-fraud-detection00489nas a2200121   4500008004100000245007500041210006900116100002100185700002000206700002000226700001900246856010200265       1999                        eng d00aExperiences with a Logic-based knowledge discovery Support Environment0 aExperiences with a Logicbased knowledge discovery Support Enviro1 aGiannotti, Fosca1 aManco, Giuseppe1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/experiences-logic-based-knowledge-discovery-support-environment-000511nas a2200133   4500008004100000245007500041210006900116300001200185100002100197700002000218700002000238700001900258856010000277       1999                        eng d00aExperiences with a Logic-Based Knowledge Discovery Support Environment0 aExperiences with a LogicBased Knowledge Discovery Support Enviro  a202-2131 aGiannotti, Fosca1 aManco, Giuseppe1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/experiences-logic-based-knowledge-discovery-support-environment00544nas a2200145   4500008004100000245007700041210006900118300001200187100002100199700002000220700001700240700002000257700001900277856010200296       1999                        eng d00aIntegration of Deduction and Induction for Mining Supermarket Sales Data0 aIntegration of Deduction and Induction for Mining Supermarket Sa  a117-1311 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/integration-deduction-and-induction-mining-supermarket-sales-data00447nas a2200109   4500008004100000245007300041210006900114300001200183100002100195700002000216856010100236       1999                        eng d00aQuerying Inductive Databases via Logic-Based User-Defined Aggregates0 aQuerying Inductive Databases via LogicBased UserDefined Aggregat  a125-1351 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/querying-inductive-databases-logic-based-user-defined-aggregates00449nas a2200109   4500008004100000245007300041210006900114300001200183100002100195700002000216856010300236       1999                        eng d00aQuerying inductive Databases via Logic-Based user-defined aggregates0 aQuerying inductive Databases via LogicBased userdefined aggregat  a605-6201 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/querying-inductive-databases-logic-based-user-defined-aggregates-000575nas a2200133   4500008004100000245011500041210006900156300001000225100002200235700002100257700002100278700002000299856012200319       1999                        eng d00aUna Metodologia Basata sulla Classificazione per la Pianificazione degli Accertamenti nel Rilevamento di Frodi0 aUna Metodologia Basata sulla Classificazione per la Pianificazio  a69-841 aBonchi, Francesco1 aGiannotti, Fosca1 aMainetto, Gianni1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/una-metodologia-basata-sulla-classificazione-la-pianificazione-degli-accertamenti-nel00477nas a2200133   4500008004100000245005900041210005900100300001200159100002200171700002100193700002100214700002000235856008800255       1999                        eng d00aUsing Data Mining Techniques in Fiscal Fraud Detection0 aUsing Data Mining Techniques in Fiscal Fraud Detection  a369-3761 aBonchi, Francesco1 aGiannotti, Fosca1 aMainetto, Gianni1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/using-data-mining-techniques-fiscal-fraud-detection00433nas a2200121   4500008004100000245006200041210006000103300001100163490000700174100002100181700002000202856008900222       1998                        eng d00aDatalog with Non-Deterministic Choice Computers NDB-PTIME0 aDatalog with NonDeterministic Choice Computers NDBPTIME  a79-1010 v351 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/datalog-non-deterministic-choice-computers-ndb-ptime00534nas a2200133   4500008004100000245009100041210006900132300001000201100002100211700002000232700001700252700002000269856011100289       1998                        eng d00aOn the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases0 aEffective Semantics of Nondeterministic Nonmonotonic Temporal Lo  a58-721 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/effective-semantics-nondeterministic-nonmonotonic-temporal-logic-databases00502nas a2200133   4500008004100000245007000041210006900111300001200180100002100192700002000213700001700233700002000250856009800270       1998                        eng d00aQuery Answering in Nondeterministic, Nonmonotonic Logic Databases0 aQuery Answering in Nondeterministic Nonmonotonic Logic Databases  a175-1871 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/query-answering-nondeterministic-nonmonotonic-logic-databases00463nas a2200133   4500008004100000245006000041210005600101300001200157100002100169700002000190700001700210700002000227856008200247       1997                        eng d00aDatalog++: a Basis for Active Object.Oriented Databases0 aDatalog a Basis for Active ObjectOriented Databases  a325-3401 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/datalog-basis-active-objectoriented-databases00464nas a2200133   4500008004100000245006000041210005600101300001200157100002100169700002000190700001700210700002000227856008300247       1997                        eng d00aDatalog++: A Basis for Active Object-Oriented Databases0 aDatalog A Basis for Active ObjectOriented Databases  a283-3011 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/datalog-basis-active-object-oriented-databases00485nas a2200121 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4500008004100000245007800041210006900119300001200188100002000200700002100220700002100241856010300262       1997                        eng d00aStatic Analysis of Transactions for Conservative Multigranularity Locking0 aStatic Analysis of Transactions for Conservative Multigranularit  a413-4301 aAmato, Giuseppe1 aGiannotti, Fosca1 aMainetto, Gianni  uhttps://kdd.isti.cnr.it/content/static-analysis-transactions-conservative-multigranularity-locking00584nas a2200133   4500008004100000245011800041210006900159300001000228100002500238700002100263700002100284700002500305856012000330       1996                        eng d00aRagionamento spazio-temporale con LDLT: primi esperimenti verso un sistema deduttivo per applicazioni geografiche0 aRagionamento spaziotemporale con LDLT primi esperimenti verso un  a73-901 aCarboni, Marilisa, E1 aDeo, Annalisa Di1 aGiannotti, Fosca1 aMasserotti, Maria, V  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a169-1821 aCarboni, Marilisa, E1 aFoddai, V1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/declarative-reconstruction-updates-logic-databases-compilative-approach-000526nas a2200133   4500008004100000245008500041210006900126300000900195100002500204700002100229700001400250700002000264856010800284       1995                        eng d00aDeclarative Reconstruction of Updates in Logic Databases: A Compilative Approach0 aDeclarative Reconstruction of Updates in Logic Databases A Compi  a3-131 aCarboni, Marilisa, E1 aGiannotti, Fosca1 aFoddai, V1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/declarative-reconstruction-updates-logic-databases-compilative-approach00415nas a2200109   4500008004100000245006500041210006200106300001200168100001800180700002100198856008600219       1994                        eng d00aAn abstract interpreter for the specification language LOTOS0 aabstract interpreter for the specification language LOTOS  a309-3231 aFiore, Franco1 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4500008004100000245008400041210006900125300001000194100002000204700002100224700002100245856010600266       1993                        eng d00aStatic Analysis of Transactions: an Experiment of Abstract Interpretation Usage0 aStatic Analysis of Transactions an Experiment of Abstract Interp  a19-291 aAmato, Giuseppe1 aGiannotti, Fosca1 aMainetto, Gianni  uhttps://kdd.isti.cnr.it/content/static-analysis-transactions-experiment-abstract-interpretation-usage00502nas a2200121   4500008004100000245008600041210006900127300001200196100002000208700002100228700002100249856011000270       1992                        eng d00aAnalysis of Concurrent Transactions in a Functional Database Programming Language0 aAnalysis of Concurrent Transactions in a Functional Database Pro  a174-1841 aAmato, Giuseppe1 aGiannotti, Fosca1 aMainetto, Gianni  uhttps://kdd.isti.cnr.it/content/analysis-concurrent-transactions-functional-database-programming-language00451nas a2200109   4500008004100000245007700041210006900118300001200187100002100199700001900220856010200239       1992                        eng d00aUsing Abstract Interpretation for Gate splitting in LOTOS Specifications0 aUsing Abstract Interpretation for Gate splitting in LOTOS Specif  a194-2041 aGiannotti, Fosca1 aLatella, Diego  uhttps://kdd.isti.cnr.it/content/using-abstract-interpretation-gate-splitting-lotos-specifications00425nas a2200133   4500008004100000245004300041210004200084300001200126100002100138700002000159700002100179700001900200856007200219       1991                        eng d00aNon-Determinism in Deductive Databases0 aNonDeterminism in Deductive Databases  a129-1461 aGiannotti, Fosca1 aPedreschi, Dino1 aSaccà, Domenico1 aZaniolo, Carlo  uhttps://kdd.isti.cnr.it/content/non-determinism-deductive-databases00481nas a2200109   4500008004100000245008700041210006900128300001200197100002100209700002800230856011300258       1991                        eng d00aA Technique for Recursive Invariance Detection and Selective Program Specification0 aTechnique for Recursive Invariance Detection and Selective Progr  a323-3341 aGiannotti, Fosca1 aHermenegildo, Manuel, V  uhttps://kdd.isti.cnr.it/content/technique-recursive-invariance-detection-and-selective-program-specification00434nas a2200109   4500008004100000245006900041210006900110300001000179100002100189700002000210856009400230       1990                        eng d00aDeclarative Semantics for Pruning Operators in Logic Programming0 aDeclarative Semantics for Pruning Operators in Logic Programming  a27-371 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/declarative-semantics-pruning-operators-logic-programming00385nas a2200109   4500008004100000245005300041210005200094300001200146100001800158700002100176856007800197       1990                        eng d00aRASP: A Resource Allocator for Software Projects0 aRASP A Resource Allocator for Software Projects  a628-6371 aBertazzoni, C1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/rasp-resource-allocator-software-projects00522nas a2200145   4500008004100000245006900041210006900110300001200179490000600191100002100197700002300218700002000241700001900261856009600280       1987                        eng d00aSymbolic Evaluation with Structural Recursive Symbolic Constants0 aSymbolic Evaluation with Structural Recursive Symbolic Constants  a161-1770 v91 aGiannotti, Fosca1 aMatteucci, Attilio1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/symbolic-evaluation-structural-recursive-symbolic-constants00456nas a2200145   4500008004100000245004500041210004500086300001200131490000700143100002300150700002100173700002000194700001900214856007700233       1985                        eng d00aSymbolic Semantics and Program Reduction0 aSymbolic Semantics and Program Reduction  a784-7940 v111 aAmbriola, Vincenzo1 aGiannotti, Fosca1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/symbolic-semantics-and-program-reduction00382nas a2200133   4500008004100000245003100041210002700072300001200099100002000111700002100131700001800152700002000170856005800190       1985                        eng d00aThe Type System of Galileo0 aType System of Galileo  a101-1191 aAlbano, Antonio1 aGiannotti, Fosca1 aOrsini, Renzo1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/type-system-galileo-000380nas a2200133   4500008004100000245003100041210002700072300001200099100002000111700002100131700001800152700002000170856005600190       1985                        eng d00aThe Type System of Galileo0 aType System of Galileo  a175-1951 aAlbano, Antonio1 aGiannotti, Fosca1 aOrsini, Renzo1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/type-system-galileo