01604nas a2200145   4500008004100000245004700041210004700088300001000135490000700145520116200152100002201314700002001336700002101356856008101377       2023                        eng d00aMobility Constraints in Segregation Models0 aMobility Constraints in Segregation Models  a120870 v133 aSince the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.1 aGambetta, Daniele1 aMauro, Giovanni1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/mobility-constraints-segregation-models01300nas a2200217   4500008004100000020001800041245008900059210006900148260005900217520057700276100002100853700001900874700001600893700002500909700002900934700001700963700002100980700001901001700001801020856004401038       2022                        eng d  a978145039529800aConnected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper)0 aConnected Vehicle Simulation Framework for Parking Occupancy Pre  aNew York, NY, USAbAssociation for Computing Machinery3 aThis paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.1 aResce, Pierpaolo1 aVorwerk, Lukas1 aHan, Zhiwei1 aCornacchia, Giuliano1 aAlamdari, Omid, Isfahani1 aNanni, Mirco1 aPappalardo, Luca1 aWeimer, Daniel1 aLiu, Yuanting  uhttps://doi.org/10.1145/3557915.356099501517nas a2200169   4500008004100000245007000041210006900111300000700180490000700187520095100194100002001145700002301165700001901188700001701207700002101224856010201245       2022                        eng d00aGenerating mobility networks with generative adversarial networks0 aGenerating mobility networks with generative adversarial network  a580 v113 aThe increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city’s entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people’s movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.1 aMauro, Giovanni1 aLuca, Massimiliano1 aLonga, Antonio1 aLepri, Bruno1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/generating-mobility-networks-generative-adversarial-networks01538nas a2200181   4500008004100000020001800041245005000059210005000109260005900159520097300218100002501191700001801216700002001234700001701254700002001271700002101291856004401312       2022                        eng d  a978145039529800aHow Routing Strategies Impact Urban Emissions0 aHow Routing Strategies Impact Urban Emissions  aNew York, NY, USAbAssociation for Computing Machinery3 aNavigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., CO2 emissions and pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.1 aCornacchia, Giuliano1 aBöhm, Matteo1 aMauro, Giovanni1 aNanni, Mirco1 aPedreschi, Dino1 aPappalardo, Luca  uhttps://doi.org/10.1145/3557915.356097701670nas a2200181   4500008004100000245006900041210006500110260001600175300001300191490000700204520109000211100002101301700001901322700002101341700001801362700003001380856007801410       2021                        eng d00aExplaining the difference between men’s and women’s football0 aExplaining the difference between men s and women s football  cApr-08-2021  ae02554070 v163 aWomen’s football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men’s football. While the two sports are often compared based on the players’ physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match’s playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men’s and women’s football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier’s decisions. The differences between men’s and women’s football are rooted in play accuracy, the recovery time of ball possession, and the players’ performance quality. Our methodology may help journalists and fans understand what makes women’s football a distinct sport and coaches design tactics tailored to female teams.1 aPappalardo, Luca1 aRossi, Alessio1 aNatilli, Michela1 aCintia, Paolo1 aConstantinou, Anthony, C.  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.025540700494nas a2200133   4500008004100000020001400041245011300055210006900168260001500237100002100252700002000273700002000293856004700313       2021                        eng d  a2364-416800aIntroduction to the special issue on social mining and big data ecosystem for open, responsible data science0 aIntroduction to the special issue on social mining and big data   c2021/03/051 aPappalardo, Luca1 aGrossi, Valerio1 aPedreschi, Dino  uhttps://doi.org/10.1007/s41060-021-00253-501965nas a2200145   4500008004100000022001400041245013400055210006900189300000800258490000700266520145600273100002501729700002101754856004401775       2021                        eng d  a2220-996400aA Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together0 aMechanistic DataDriven Approach to Synthesize Human Mobility Con  a5990 v103 aModelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.1 aCornacchia, Giuliano1 aPappalardo, Luca  uhttps://www.mdpi.com/2220-9964/10/9/59900501nas a2200109   4500008004100000245010900041210006900150260000700219100002500226700002100251856011900272       2021                        eng d00aSTS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together0 aSTSEPR Modelling individual mobility considering the spatial tem  c051 aCornacchia, Giuliano1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/sts-epr-modelling-individual-mobility-considering-spatial-temporal-and-social01528nas 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/2004.1127801545nas a2200169   4500008004100000020001400041245006400055210006400119260000900183300001100192520103400203100002501237700002101262700001501283700001901298856005801317       2020                        eng d  a1558-001600aModeling Adversarial Behavior Against Mobility Data Privacy0 aModeling Adversarial Behavior Against Mobility Data Privacy  c2020  a1 - 143 aPrivacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way.1 aPellungrini, Roberto1 aPappalardo, Luca1 aSimini, F.1 aMonreale, Anna  uhttps://ieeexplore.ieee.org/abstract/document/919989300485nas a2200109   4500008004100000245008100041210006900122100002500191700002100216700002100237856011700258       2020                        eng d00aModelling Human Mobility considering Spatial, Temporal and Social Dimensions0 aModelling Human Mobility considering Spatial Temporal and Social1 aCornacchia, Giuliano1 aRossetti, Giulio1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/modelling-human-mobility-considering-spatial-temporal-and-social-dimensions03169nas 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/2006.0314100539nas a2200109   4500008004100000245012600041210006900167100002200236700002100258700002300279856012700302       2019                        eng d00aDefining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data0 aDefining Geographic Markets from Probabilistic Clusters A Machin1 aBruestle, Stephen1 aPappalardo, Luca1 aGuidotti, Riccardo  uhttps://kdd.isti.cnr.it/publications/defining-geographic-markets-probabilistic-clusters-machine-learning-algorithm-applied01227nas a2200133   4500008004100000245007400041210006900115520077700184100002100961700002200982700002501004700002001029856004401049       2019                        eng d00aHuman Mobility from theory to practice: Data, Models and Applications0 aHuman Mobility from theory to practice Data Models and Applicati3 aThe inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library ”scikit-mobility” developed by the presenters of the tutorial.1 aPappalardo, Luca1 aBarlacchi, Gianni1 aPellungrini, Roberto1 aSimini, Filippo  uhttps://doi.org/10.1145/3308560.332009901999nas 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/s41597-019-0247-701847nas a2200169   4500008004100000245014600041210006900187300000900256490000600265520126200271100001901533700001801552700002101570700001801591700001901609856004901628       2019                        eng d00aRelationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load0 aRelationship between External and Internal Workloads in Elite So  a51740 v93 aThe use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.1 aRossi, Alessio1 aPerri, Enrico1 aPappalardo, Luca1 aCintia, Paolo1 aIaia, Marcello  uhttps://www.mdpi.com/2076-3417/9/23/5174/htm01262nas a2200133   4500008004100000245005000041210005000091520085000141100002500991700002101016700002301037700001901060856004901079       2018                        eng d00aAnalyzing Privacy Risk in Human Mobility Data0 aAnalyzing Privacy Risk in Human Mobility Data3 aMobility data are of fundamental importance for understanding the patterns of human movements, developing analytical services and modeling human dynamics. Unfortunately, mobility data also contain individual sensitive information, making it necessary an accurate privacy risk assessment for the individuals involved. In this paper, we propose a methodology for assessing privacy risk in human mobility data. Given a set of individual and collective mobility features, we define the minimum data format necessary for the computation of each feature and we define a set of possible attacks on these data formats. We perform experiments computing the empirical risk in a real-world mobility dataset, and show how the distributions of the considered mobility features are affected by the removal of individuals with different levels of privacy risk.1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttps://doi.org/10.1007/978-3-030-04771-9_1001671nas a2200181   4500008004100000245008700041210006900128300001300197490000700210520107500217100001901292700002101311700001801332700001901350700002301369700001901392856007801411       2018                        eng d00aEffective injury forecasting in soccer with GPS training data and machine learning0 aEffective injury forecasting in soccer with GPS training data an  ae02012640 v133 aInjuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.1 aRossi, Alessio1 aPappalardo, Luca1 aCintia, Paolo1 aIaia, Marcello1 aFernàndez, Javier1 aMedina, Daniel  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.020126400558nas a2200133   4500008004100000245009000041210006900131260001300200100002100213700001900234700002300253700002100276856012700297       2018                        eng d00aExploring Students Eating Habits Through Individual Profiling and Clustering Analysis0 aExploring Students Eating Habits Through Individual Profiling an  bSpringer1 aNatilli, Michela1 aMonreale, Anna1 aGuidotti, Riccardo1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/exploring-students-eating-habits-through-individual-profiling-and-clustering-analysis03273nas a2200229   4500008004100000245015000041210006900191300000800260490000700268520247100275100002202746700002102768700002502789700002102814700001902835700002402854700002002878700002002898700002002918700002302938856008202961       2018                        eng d00aGastroesophageal reflux symptoms among Italian university students: epidemiology and dietary correlates using automatically recorded transactions0 aGastroesophageal reflux symptoms among Italian university studen  a1160 v183 aBackground: Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal disorders
worldwide, with relevant impact on the quality of life and health care costs.The aim of our study is to assess the
prevalence of GERD based on self-reported symptoms among university students in central Italy. The secondary aim is
to evaluate lifestyle correlates, particularly eating habits, in GERD students using automatically recorded transactions
through cashiers at university canteen.
Methods: A web-survey was created and launched through an app, ad-hoc developed for an interactive exchange of
information with students, including anthropometric data and lifestyle habits. Moreover, the web-survey allowed
users a self-diagnosis of GERD through a simple questionnaire. As regard eating habits, detailed collection of meals
consumed, including number and type of dishes, were automatically recorded through cashiers at the university
canteen equipped with an automatic registration system.
Results: We collected 3012 questionnaires. A total of 792 students (26.2% of the respondents) reported typical GERD
symptoms occurring at least weekly. Female sex was more prevalent than male sex. In the set of students with GERD,
the percentage of smokers was higher, and our results showed that when BMI tends to higher values the percentage
of students with GERD tends to increase. When evaluating correlates with diet, we found, among all users, a lower
frequency of legumes choice in GERD students and, among frequent users, a lower frequency of choice of pasta and
rice in GERD students.
Discussion: The results of our study are in line with the values reported in the literature. Nowadays, GERD is a common
problem in our communities, and can potentially lead to serious medical complications; the economic burden
involved in the diagnostic and therapeutic management of the disease has a relevant impact on healthcare costs.
Conclusions: To our knowledge, this is the first study evaluating the prevalence of typical GERD–related symptoms
in a young population of University students in Italy. Considering the young age of enrolled subjects, our prevalence
rate, relatively high compared to the usual estimates, could represent a further negative factor for the future
economic sustainability of the healthcare system.
Keywords: Gastroesophageal reflux disease, GERD, Heartburn, Regurgitation, Diet, Prevalence, University students1 aMartinucci, Irene1 aNatilli, Michela1 aLorenzoni, Valentina1 aPappalardo, Luca1 aMonreale, Anna1 aTurchetti, Giuseppe1 aPedreschi, Dino1 aMarchi, Santino1 aBarale, Roberto1 ade Bortoli, Nicola  uhttps://bmcgastroenterol.biomedcentral.com/articles/10.1186/s12876-018-0832-901833nas a2200169   4500008004100000245005500041210005500096260000700151300000900158490000700167520134100174100002701515700002101542700002301563700002801586856004901614       2018                        eng d00aGravity and scaling laws of city to city migration0 aGravity and scaling laws of city to city migration  c07  a1-190 v133 aModels of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.1 aCuriel, Rafael, Prieto1 aPappalardo, Luca1 aGabrielli, Lorenzo1 aBishop, Steven, Richard  uhttps://doi.org/10.1371/journal.pone.019989201581nas 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_1700599nas 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/847715701714nas a2200157   4500008004100000245009000041210006900131260000900200520116900209100002201378700002201400700002101422700002201443700001701465856007401482       2018                        eng d00aWeak nodes detection in urban transport systems: Planning for resilience in Singapore0 aWeak nodes detection in urban transport systems Planning for res  bIEEE3 aThe availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc. . . ). In this perspective, we propose ACHILLES, an application to models people's movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.1 aFerretti, Michele1 aBarlacchi, Gianni1 aPappalardo, Luca1 aLucchini, Lorenzo1 aLepri, Bruno  uhttps://ieeexplore.ieee.org/abstract/document/8631413/authors#authors00999nas a2200121   4500008004100000245004200041210004200083520063500125100002500760700002300785700002100808856004800829       2017                        eng d00aAssessing Privacy Risk in Retail Data0 aAssessing Privacy Risk in Retail Data3 aRetail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.1 aPellungrini, Roberto1 aPratesi, Francesca1 aPappalardo, Luca  uhttps://doi.org/10.1007/978-3-319-71970-2_301645nas a2200169   4500008004100000022001400041245007300055210006900128300001700197490000600214520112800220100002501348700002101373700002301394700001901417856003901436       2017                        eng d  a2157-690400aA Data Mining Approach to Assess Privacy Risk in Human Mobility Data0 aData Mining Approach to Assess Privacy Risk in Human Mobility Da  a31:1–31:270 v93 aHuman mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttp://doi.acm.org/10.1145/310677401949nas a2200133   4500008004100000022001400041245007300055210006900128260000800197520152300205100002101728700002001749856004601769       2017                        eng d  a1573-756X00aData-driven generation of spatio-temporal routines in human mobility0 aDatadriven generation of spatiotemporal routines in human mobili  cDec3 aThe generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present Ditras (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. Ditras operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate Ditras with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating Ditras with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.1 aPappalardo, Luca1 aSimini, Filippo  uhttps://doi.org/10.1007/s10618-017-0548-401483nas a2200145   4500008004100000020002200041245005900063210005900122520097800181100002501159700002101184700002301205700001901228856009001247       2017                        eng d  a978-3-319-66283-100aFast Estimation of Privacy Risk in Human Mobility Data0 aFast Estimation of Privacy Risk in Human Mobility Data3 aMobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual’s mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods. 1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttps://kdd.isti.cnr.it/publications/fast-estimation-privacy-risk-human-mobility-data01536nas 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-sequences01626nas 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/1702.0715801507nas a2200121   4500008004100000245007100041210006900112300001200181520108400193100002101277700001801298856006901316       2017                        eng d00aQuantifying the relation between performance and success in soccer0 aQuantifying the relation between performance and success in socc  a17500143 aThe availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team’s position in a competition’s final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team’s performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems’ view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.1 aPappalardo, Luca1 aCintia, Paolo  uhttp://www.worldscientific.com/doi/abs/10.1142/S021952591750014X01804nas 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-data01559nas 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-services01443nas a2200133   4500008004100000245008000041210006900121260003500190520094100225100002101166700002101187700001801208856008301226       2016                        eng d00aA novel approach to evaluate community detection algorithms on ground truth0 anovel approach to evaluate community detection algorithms on gro  aDijon, FrancebSpringer-Verlag3 aEvaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.1 aRossetti, Giulio1 aPappalardo, Luca1 aRinzivillo, S  uhttp://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf00574nas 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=2808797.280938402029nas 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.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/ncomms916600674nas 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-sources00447nas a2200109   4500008004100000245006800041210006700109100001800176700002100194700002000215856010200235       2014                        eng d00aMining efficient training patterns of non-professional cyclists0 aMining efficient training patterns of nonprofessional cyclists1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/mining-efficient-training-patterns-non-professional-cyclists00482nas 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/62300505nas 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.2014.705809000483nas 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-car00410nas a2200109   4500008004100000245008700041210006900128100001800197700002100215700002000236856004400256       2013                        eng d00a"Engine Matters": {A} First Large Scale Data Driven Study on Cyclists' Performance0 aEngine Matters A First Large Scale Data Driven Study on Cyclists1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino  uhttp://dx.doi.org/10.1109/ICDMW.2013.4100434nas a2200121   4500008003900000245005600039210005600095260000900151100002100160700002100181700002000202856009000222       2013                          d00aMeasuring tie strength in multidimensional networks0 aMeasuring tie strength in multidimensional networks  c20131 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/measuring-tie-strength-multidimensional-networks00453nas 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%252fe2013-01715-500515nas 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-eterogenei00442nas a2200109   4500008004100000245009700041210006900138100002100207700002100228700002000249856006300269       2012                        eng d00a"How Well Do We Know Each Other?" Detecting Tie Strength in Multidimensional Social Networks0 aHow Well Do We Know Each Other Detecting Tie Strength in Multidi1 aPappalardo, Luca1 aRossetti, Giulio1 aPedreschi, Dino  uhttp://doi.ieeecomputersociety.org/10.1109/ASONAM.2012.180