%0 Conference Paper %B International Symposium on Intelligent Data Analysis %D 2020 %T Digital Footprints of International Migration on Twitter %A Jisu Kim %A Alina Sirbu %A Fosca Giannotti %A Lorenzo Gabrielli %X Studying 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. %B International Symposium on Intelligent Data Analysis %I Springer %G eng %U https://link.springer.com/chapter/10.1007/978-3-030-44584-3_22 %R https://doi.org/10.1007/978-3-030-44584-3_22 %0 Conference Paper %B Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency %D 2020 %T Doctor XAI: an ontology-based approach to black-box sequential data classification explanations %A Cecilia Panigutti %A Perotti, Alan %A Dino Pedreschi %X Several recent advancements in Machine Learning involve black-box models: algorithms that do not provide human-understandable explanations in support of their decisions. This limitation hampers the fairness, accountability and transparency of these models; the field of eXplainable Artificial Intelligence (XAI) tries to solve this problem providing human-understandable explanations for black-box models. However, healthcare datasets (and the related learning tasks) often present peculiar features, such as sequential data, multi-label predictions, and links to structured background knowledge. In this paper, we introduce Doctor XAI, a model-agnostic explainability technique able to deal with multi-labeled, sequential, ontology-linked data. We focus on explaining Doctor AI, a multilabel classifier which takes as input the clinical history of a patient in order to predict the next visit. Furthermore, we show how exploiting the temporal dimension in the data and the domain knowledge encoded in the medical ontology improves the quality of the mined explanations. %B Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency %G eng %U https://dl.acm.org/doi/pdf/10.1145/3351095.3372855?download=true %R 10.1145/3351095.3372855 %0 Journal Article %J Available at SSRN 3452058 %D 2019 %T Defining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data %A Bruestle, Stephen %A Luca Pappalardo %A Riccardo Guidotti %B Available at SSRN 3452058 %G eng %0 Journal Article %J Frontiers in Psychology %D 2019 %T Do “girls just wanna have fun”? Participation trends and motivational profiles of women in Norway’s ultimate mass participation ski event %A Calogiuri, Giovanna %A Johansen, Patrick Foss %A Alessio Rossi %A Thurston, Miranda %X Mass participation sporting events (MPSEs) are viewed as encouraging regular exercise in the population, but concerns have been expressed about the extent to which they are inclusive for women. This study focuses on an iconic cross-country skiing MPSE in Norway, the Birkebeiner race (BR), which includes different variants (main, Friday, half-distance, and women-only races). In order to shed light on women’s participation in this specific MPSE, as well as add to the understanding of women’s MPSEs participation in general, this study was set up to: (i) analyze trends in women’s participation, (ii) examine the characteristics, and (iii) identify key factors characterizing the motivational profile of women in different BR races, with emphasis on the full-distance vs. the women-only races. Entries in the different races throughout the period 1996–2018 were analyzed using an autoregressive model. Information on women’s sociodemographic characteristics, sport and exercise participation, and a range of psychological variables (motives, perceptions, overall satisfaction, and future participation intention) were extracted from a market survey and analyzed using a machine learning (ML) approach (n = 1,149). Additionally, qualitative information generated through open-ended questions was analyzed thematically (n = 116). The relative prevalence of women in the main BR was generally low (< 20%). While the other variants contributed to boosting women’s participation in the overall event, a future increment of women in the main BR was predicted, with women’s ratings possibly matching the men’s by the year 2034. Across all races, most of the women were physically active, of medium-high income, and living in the most urbanized region of Norway. Satisfaction and future participation intention were relatively high, especially among the participants in the women-only races. “Exercise goal” was the predominant participation motive. The participants in women-only races assigned greater importance to social aspects, and perceived the race as a tradition, whereas those in the full-distance races were younger and gave more importance to performance aspects. These findings corroborate known trends and challenges in MPSE participation, but also contribute to greater understanding in this under-researched field. Further research is needed in order to gain more knowledge on how to foster women’s participation in MPSEs. %B Frontiers in Psychology %V 10 %P 2548 %G eng %U https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02548/full %R 10.3389/fpsyg.2019.02548 %0 Journal Article %J arXiv preprint arXiv:1905.01498 %D 2019 %T DynComm R Package–Dynamic Community Detection for Evolving Networks %A Sarmento, Rui Portocarrero %A Lemos, Luís %A Cordeiro, Mário %A Giulio Rossetti %A Cardoso, Douglas %B arXiv preprint arXiv:1905.01498 %G eng %0 Conference Paper %B International Conference on Complex Networks CompleNet %D 2018 %T Diffusive Phenomena in Dynamic Networks: a data-driven study %A Letizia Milli %A Giulio Rossetti %A Dino Pedreschi %A Fosca Giannotti %X Everyday, 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. %B International Conference on Complex Networks CompleNet %I Springer %C Boston March 5-8 2018 %G eng %U https://link.springer.com/chapter/10.1007/978-3-319-73198-8_13 %R 10.1007/978-3-319-73198-8_13 %0 Conference Paper %B International Workshop on Complex Networks %D 2018 %T Discovering Mobility Functional Areas: A Mobility Data Analysis Approach %A Lorenzo Gabrielli %A Daniele Fadda %A Giulio Rossetti %A Mirco Nanni %A Piccinini, Leonardo %A Dino Pedreschi %A Fosca Giannotti %A Patrizia Lattarulo %X How 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. %B International Workshop on Complex Networks %I Springer %G eng %U https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27 %R 10.1007/978-3-319-73198-8_27 %0 Journal Article %J EPJ Data Science %D 2018 %T Discovering temporal regularities in retail customers’ shopping behavior %A Riccardo Guidotti %A Lorenzo Gabrielli %A Anna Monreale %A Dino Pedreschi %A Fosca Giannotti %X In 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. %B EPJ Data Science %V 7 %P 6 %8 01/2018 %G eng %U https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0133-0 %R 10.1140/epjds/s13688-018-0133-0 %0 Journal Article %J ACM Trans. Intell. Syst. Technol. %D 2017 %T A Data Mining Approach to Assess Privacy Risk in Human Mobility Data %A Roberto Pellungrini %A Luca Pappalardo %A Francesca Pratesi %A Anna Monreale %X Human 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. %B ACM Trans. Intell. Syst. Technol. %V 9 %P 31:1–31:27 %G eng %U http://doi.acm.org/10.1145/3106774 %R 10.1145/3106774 %0 Unpublished Work %D 2017 %T Data Science a Game-changer for Science and Innovation %A Fabio Beltram %A Fosca Giannotti %A Dino Pedreschi %X Digital 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. %I G7 Academy %8 03/2017 %G eng %0 Journal Article %J Data Mining and Knowledge Discovery %D 2017 %T Data-driven generation of spatio-temporal routines in human mobility %A Luca Pappalardo %A Filippo Simini %X The 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. %B Data Mining and Knowledge Discovery %8 Dec %G eng %U https://doi.org/10.1007/s10618-017-0548-4 %R 10.1007/s10618-017-0548-4 %0 Journal Article %J Information %D 2017 %T Discovering and Understanding City Events with Big Data: The Case of Rome %A Barbara Furletti %A Roberto Trasarti %A Paolo Cintia %A Lorenzo Gabrielli %X The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists. %B Information %V 8 %P 74 %8 06/2017 %G eng %U https://doi.org/10.3390/info8030074 %R 10.3390/info8030074 %0 Conference Paper %B European Conference on Parallel Processing %D 2017 %T Dynamic community analysis in decentralized online social networks %A Guidi, Barbara %A Michienzi, Andrea %A Giulio Rossetti %B European Conference on Parallel Processing %I Springer %G eng %0 Generic %D 2016 %T Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach. %A Bessiere, Christian %A De Raedt, Luc %A Lars Kotthoff %A Siegfried Nijssen %A Barry O'Sullivan %A Dino Pedreschi %X A successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge. This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases. %G eng %R 10.1007/978-3-319-50137-6 %0 Book Section %B Data Mining and Constraint Programming %D 2016 %T Data Mining and Constraints: An Overview %A Valerio Grossi %A Dino Pedreschi %A Franco Turini %X This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints requires mechanisms for defining and evaluating them during the knowledge extraction process. We give a structured account of three main groups of constraints based on the specific context in which they are defined and used. The aim is to provide a complete view on constraints as a building block of data mining methods. %B Data Mining and Constraint Programming %I Springer International Publishing %P 25–48 %G eng %R 10.1007/978-3-319-50137-6_2 %0 Journal Article %J Journal ACM Transactions on Intelligent Systems and Technology (TIST) %D 2016 %T Driving Profiles Computation and Monitoring for Car Insurance CRM %A Mirco Nanni %A Roberto Trasarti %A Anna Monreale %A Valerio Grossi %A Dino Pedreschi %X Customer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, we explore the problem in the context of the car insurance industry, where the mobility behavior of customers plays a key role: Different mobility needs, driving habits, and skills imply also different requirements (level of coverage provided by the insurance) and risks (of accidents). In the present work, we describe a methodology to extract several indicators describing the driving profile of customers, and we provide a clustering-oriented instantiation of the segmentation problem based on such indicators. Then, we consider the availability of a continuous flow of fresh mobility data sent by the circulating vehicles, aiming at keeping our segments constantly up to date. We tackle a major scalability issue that emerges in this context when the number of customers is large-namely, the communication bottleneck-by proposing and implementing a sophisticated distributed monitoring solution that reduces communications between vehicles and company servers to the essential. We validate the framework on a large database of real mobility data coming from GPS devices on private cars. Finally, we analyze the privacy risks that the proposed approach might involve for the users, providing and evaluating a countermeasure based on data perturbation. %B Journal ACM Transactions on Intelligent Systems and Technology (TIST) %V 8 %P 14:1–14:26 %G eng %U http://doi.acm.org/10.1145/2912148 %R 10.1145/2912148 %0 Journal Article %J Microarrays %D 2015 %T Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks %A Alina Sirbu %A Martin Crane %A Heather J Ruskin %B Microarrays %V 4 %P 255–269 %G eng %U http://www.mdpi.com/2076-3905/4/2/255 %R 10.3390/microarrays4020255 %0 Conference Paper %B NetMob %D 2015 %T Detecting and understanding big events in big cities %A Barbara Furletti %A Lorenzo Gabrielli %A Roberto Trasarti %A Zbigniew Smoreda %A Maarten Vanhoof %A Cezary Ziemlicki %X Recent studies have shown the great potential of big data such as mobile phone location data to model human behavior. Big data allow to analyze people presence in a territory in a fast and effective way with respect to the classical surveys (diaries or questionnaires). One of the drawbacks of these collection systems is incompleteness of the users' traces; people are localized only when they are using their phones. In this work we define a data mining method for identifying people presence and understanding the impact of big events in big cities. We exploit the ability of the Sociometer for classifying mobile phone users in mobility categories through their presence profile. The experiment in cooperation with Orange Telecom has been conduced in Paris during the event F^ete de la Musique using a privacy preserving protocol. %B NetMob %C Boston %8 04/2015 %G eng %U http://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf %0 Journal Article %J Data Min. Knowl. Discov. %D 2015 %T Discrimination- and privacy-aware patterns %A Sara Hajian %A Josep Domingo-Ferrer %A Anna Monreale %A Dino Pedreschi %A Fosca Giannotti %X Data 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. %B Data Min. Knowl. Discov. %V 29 %P 1733–1782 %G eng %U http://dx.doi.org/10.1007/s10618-014-0393-7 %R 10.1007/s10618-014-0393-7 %0 Journal Article %J Concurrency and Computation: Practice and Experience %D 2014 %T Decision tree building on multi-core using FastFlow %A Aldinucci, Marco %A Salvatore Ruggieri %A Torquati, Massimo %X The whole computer hardware industry embraced the multi-core. The extreme optimisation of sequential algorithms is then no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an in-depth study of the parallelisation of an implementation of the C4.5 algorithm for multi-core architectures. We characterise elapsed time lower bounds for the forms of parallelisations adopted and achieve close to optimal performance. Our implementation is based on the FastFlow parallel programming environment, and it requires minimal changes to the original sequential code. Copyright © 2013 John Wiley & Sons, Ltd. %B Concurrency and Computation: Practice and Experience %V 26 %P 800–820 %G eng %R 10.1002/cpe.3063 %0 Journal Article %J Telecommunications Policy %D 2014 %T Discovering urban and country dynamics from mobile phone data with spatial correlation patterns %A Roberto Trasarti %A Ana-Maria Olteanu-Raimond %A Mirco Nanni %A Thomas Couronné %A Barbara Furletti %A Fosca Giannotti %A Zbigniew Smoreda %A Cezary Ziemlicki %K Urban dynamics %X Abstract 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. %B Telecommunications Policy %P - %U http://www.sciencedirect.com/science/article/pii/S0308596113002012 %R http://dx.doi.org/10.1016/j.telpol.2013.12.002 %0 Conference Paper %B Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on %D 2013 %T Data Anonymity Meets Non-discrimination %A Salvatore Ruggieri %B Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on %I IEEE %G eng %0 Book Section %B Discrimination and privacy in the information society %D 2013 %T The discovery of discrimination %A Dino Pedreschi %A Salvatore Ruggieri %A Franco Turini %B Discrimination and privacy in the information society %I Springer %P 91–108 %G eng %0 Journal Article %J Expert Systems with Applications %D 2013 %T Discrimination discovery in scientific project evaluation: A case study %A Andrea Romei %A Salvatore Ruggieri %A Franco Turini %B Expert Systems with Applications %V 40 %P 6064–6079 %G eng %0 Journal Article %J KI - Künstliche Intelligenz %D 2012 %T Data Science for Simulating the Era of Electric Vehicles %A Davy Janssens %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A S Rinzivillo %B KI - Künstliche Intelligenz %R 10.1007/s13218-012-0183-6 %0 Conference Paper %B KDD 2012 %D 2012 %T DEMON: a Local-First Discovery Method for Overlapping Communities %A Michele Coscia %A Giulio Rossetti %A Fosca Giannotti %A Dino Pedreschi %B KDD 2012 %8 2012 %0 Conference Paper %B The 18th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, {KDD} '12, Beijing, China, August 12-16, 2012 %D 2012 %T DEMON: a local-first discovery method for overlapping communities %A Michele Coscia %A Giulio Rossetti %A Fosca Giannotti %A Dino Pedreschi %B The 18th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, {KDD} '12, Beijing, China, August 12-16, 2012 %G eng %U http://doi.acm.org/10.1145/2339530.2339630 %R 10.1145/2339530.2339630 %0 Journal Article %J KI - Künstliche Intelligenz %D 2012 %T Discovering the Geographical Borders of Human Mobility %A S Rinzivillo %A Simone Mainardi %A Fabio Pezzoni %A Michele Coscia %A Fosca Giannotti %A Dino Pedreschi %X The 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. %B KI - Künstliche Intelligenz %U https://link.springer.com/article/10.1007%2Fs13218-012-0181-8 %& 1 %R 10.1007/s13218-012-0181-8 %0 Book %D 2011 %T Dinamiche di impoverimento. Meccanismi, traiettorie ed effetti in un contesto locale %A Tomei, Gabriele %A Michela Natilli %I Carocci Editore %G eng %0 Conference Paper %B SEBD %D 2010 %T Discovering Eras in Evolving Social Networks (Extended Abstract) %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %B SEBD %P 78-85 %0 Conference Paper %B DEXA Workshops %D 2009 %T DAMSEL: A System for Progressive Querying and Reasoning on Movement Data %A Roberto Trasarti %A Miriam Baglioni %A Chiara Renso %B DEXA Workshops %P 452-456 %0 Journal Article %D 2009 %T Developing a Spatial Knowledge Representation for Pedestrian Interactions %A Daniel Ornellana, Chiara Renso %G eng %0 Conference Paper %B SEBD %D 2008 %T DAEDALUS: A knowledge discovery analysis framework for movement data %A Riccardo Ortale %A E Ritacco %A N. Pelekisy %A Roberto Trasarti %A Gianni Costa %A Fosca Giannotti %A Giuseppe Manco %A Chiara Renso %A Yannis Theodoridis %B SEBD %P 191-198 %G eng %0 Conference Paper %B GIS %D 2008 %T The DAEDALUS framework: progressive querying and mining of movement data %A Riccardo Ortale %A E Ritacco %A Nikos Pelekis %A Roberto Trasarti %A Gianni Costa %A Fosca Giannotti %A Giuseppe Manco %A Chiara Renso %A Yannis Theodoridis %B GIS %P 52 %0 Book Section %B Advances in Robotics, Automation and Control %D 2008 %T Discovering Strategic Behaviour in Multi- Agent Scenarios by Ontology-Driven Mining %A Davide Bacciu %A Andrea Bellandi %A Barbara Furletti %A Valerio Grossi %A Andrea Romei %B Advances in Robotics, Automation and Control %@ 978-953-7619-16-9 %U http://www.intechopen.com/books/advances_in_robotics_automation_and_control/discovering_strategic_behaviors_in_multi-agent_scenarios_by_ontology-driven_mining %0 Conference Paper %B KDD %D 2008 %T Discrimination-aware data mining %A Dino Pedreschi %A Salvatore Ruggieri %A Franco Turini %B KDD %P 560-568 %0 Conference Paper %B ACM Symposium on Applied Computing %D 2005 %T DrC4.5: Improving C4.5 by means of Prior Knowledge %A Miriam Baglioni %A Barbara Furletti %A Franco Turini %B ACM Symposium on Applied Computing %I ACM %C Santa Fe, New Mexico, USA %@ 1-58113-964-0 %U http://dl.acm.org/ft_gateway.cfm?id=1066787&ftid=311609&dwn=1&CFID=96873366&CFTOKEN=59233511 %R http://dx.doi.org/10.1145/1066677.1066787 %0 Conference Paper %B INAP/WLP %D 2004 %T Deductive and Inductive Reasoning on Spatio-Temporal Data %A Mirco Nanni %A Alessandra Raffaetà %A Chiara Renso %A Franco Turini %B INAP/WLP %P 98-115 %G eng %0 Conference Paper %B SEBD %D 2004 %T Deductive and Inductive Reasoning on Trajectories %A Mirco Nanni %A Alessandra Raffaetà %A Chiara Renso %A Franco Turini %B SEBD %P 98-105 %G eng %0 Conference Paper %B DMKD %D 2004 %T Discovery of ads web hosts through traffic data analysis %A V. Bacarella %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %B DMKD %P 76-81 %G eng %0 Conference Paper %B Computational Logic: Logic Programming and Beyond %D 2002 %T The Declarative Side of Magic %A Paolo Mascellani %A Dino Pedreschi %B Computational Logic: Logic Programming and Beyond %P 83-108 %G eng %0 Conference Paper %B ITCC %D 2001 %T Data Mining for Intelligent Web Caching %A Francesco Bonchi %A Fosca Giannotti %A Giuseppe Manco %A Chiara Renso %A Mirco Nanni %A Dino Pedreschi %A Salvatore Ruggieri %B ITCC %P 599-603 %G eng %0 Conference Paper %B ITCC %D 2001 %T Data Mining for Intelligent Web Caching %A Francesco Bonchi %A Fosca Giannotti %A Giuseppe Manco %A Chiara Renso %A Mirco Nanni %A Dino Pedreschi %A Salvatore Ruggieri %B ITCC %P 599-603 %G eng %0 Conference Paper %B FQAS %D 2000 %T Declarative Knowledge Extraction with Interactive User-Defined Aggregates %A Fosca Giannotti %A Giuseppe Manco %B FQAS %P 435-444 %G eng %0 Journal Article %J Comput. Lang. %D 1999 %T Dynamic composition of parameterised logic modules %A Antonio Brogi %A Chiara Renso %A Franco Turini %B Comput. Lang. %V 25 %P 211-242 %G eng %0 Journal Article %J Computer Languages %D 1999 %T Dynamic Composition of Parameterised Logic Modules %A Antonio Brogi %A Chiara Renso %A Franco Turini %B Computer Languages %P 211–242 %G eng %0 Journal Article %J J. Log. Program. %D 1998 %T Datalog with Non-Deterministic Choice Computers NDB-PTIME %A Fosca Giannotti %A Dino Pedreschi %B J. Log. Program. %V 35 %P 79-101 %G eng %0 Conference Paper %B SEBD %D 1997 %T Datalog++: a Basis for Active Object.Oriented Databases %A Fosca Giannotti %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %B SEBD %P 325-340 %G eng %0 Conference Paper %B DOOD %D 1997 %T Datalog++: A Basis for Active Object-Oriented Databases %A Fosca Giannotti %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %B DOOD %P 283-301 %G eng %0 Conference Paper %B APPIA-GULP-PRODE %D 1997 %T A Deductive Data Model for Representing and Querying Semistructured Data %A Fosca Giannotti %A Giuseppe Manco %A Dino Pedreschi %B APPIA-GULP-PRODE %P 129-140 %G eng %0 Conference Paper %B SEBD %D 1995 %T Declarative Reconstruction of Updates in Logic Databases: A Compilative Approach %A Marilisa E. Carboni %A Fosca Giannotti %A V. Foddai %A Dino Pedreschi %B SEBD %P 3-13 %G eng %0 Conference Paper %B GULP-PRODE %D 1995 %T Declarative Reconstruction of Updates in Logic Databases: a Compilative Approach %A Marilisa E. Carboni %A V. Foddai %A Fosca Giannotti %A Dino Pedreschi %B GULP-PRODE %P 169-182 %G eng %0 Conference Paper %B VLDB %D 1993 %T Data Sharing Analysis for a Database Programming Lanaguage via Abstract Interpretation %A Giuseppe Amato %A Fosca Giannotti %A Gianni Mainetto %B VLDB %P 405-415 %G eng %0 Conference Paper %B DOOD %D 1993 %T Datalog with Non-Deterministic Choice Computes NDB-PTIME %A Luca Corciulo %A Fosca Giannotti %A Dino Pedreschi %B DOOD %P 49-66 %G eng %0 Conference Paper %B LPNMR %D 1990 %T Declarative Semantics for Pruning Operators in Logic Programming %A Fosca Giannotti %A Dino Pedreschi %B LPNMR %P 27-37 %G eng