@conference {1323,
	title = {Digital Footprints of International Migration on Twitter},
	booktitle = {International Symposium on Intelligent Data Analysis},
	year = {2020},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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{\textquoteright}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.},
	doi = {https://doi.org/10.1007/978-3-030-44584-3_22},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-44584-3_22},
	author = {Jisu Kim and Alina Sirbu and Fosca Giannotti and Lorenzo Gabrielli}
}
@conference {1021,
	title = {Diffusive Phenomena in Dynamic Networks: a data-driven study},
	booktitle = {International Conference on Complex Networks CompleNet},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	address = {Boston March 5-8 2018},
	abstract = {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 {\textendash} following a data-driven approach {\textendash} 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.},
	doi = {10.1007/978-3-319-73198-8_13},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_13},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@conference {1046,
	title = {Discovering Mobility Functional Areas: A Mobility Data Analysis Approach},
	booktitle = {International Workshop on Complex Networks},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-319-73198-8_27},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27},
	author = {Lorenzo Gabrielli and Daniele Fadda and Giulio Rossetti and Mirco Nanni and Piccinini, Leonardo and Dino Pedreschi and Fosca Giannotti and Patrizia Lattarulo}
}
@article {1130,
	title = {Discovering temporal regularities in retail customers{\textquoteright} shopping behavior},
	journal = {EPJ Data Science},
	volume = {7},
	number = {1},
	year = {2018},
	month = {01/2018},
	pages = {6},
	abstract = {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{\textquoteright}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.},
	doi = {10.1140/epjds/s13688-018-0133-0},
	url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0133-0},
	author = {Riccardo Guidotti and Lorenzo Gabrielli and Anna Monreale and Dino Pedreschi and Fosca Giannotti}
}
@article {759,
	title = {Discrimination- and privacy-aware patterns},
	journal = {Data Min. Knowl. Discov.},
	volume = {29},
	number = {6},
	year = {2015},
	pages = {1733{\textendash}1782},
	abstract = {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. },
	doi = {10.1007/s10618-014-0393-7},
	url = {http://dx.doi.org/10.1007/s10618-014-0393-7},
	author = {Sara Hajian and Josep Domingo-Ferrer and Anna Monreale and Dino Pedreschi and Fosca Giannotti}
}
@article {Trasarti2014,
	title = {Discovering urban and country dynamics from mobile phone data with spatial correlation patterns},
	journal = {Telecommunications Policy},
	year = {2014},
	pages = {-},
	abstract = {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.},
	keywords = {Urban dynamics},
	issn = {0308-5961},
	doi = {http://dx.doi.org/10.1016/j.telpol.2013.12.002},
	url = {http://www.sciencedirect.com/science/article/pii/S0308596113002012},
	author = {Roberto Trasarti and Ana-Maria Olteanu-Raimond and Mirco Nanni and Thomas Couronn{\'e} and Barbara Furletti and Fosca Giannotti and Zbigniew Smoreda and Cezary Ziemlicki}
}
@article {462,
	title = {Data Science for Simulating the Era of Electric Vehicles},
	journal = {KI - K{\"u}nstliche Intelligenz},
	year = {2012},
	doi = {10.1007/s13218-012-0183-6},
	author = {Davy Janssens and Fosca Giannotti and Mirco Nanni and Dino Pedreschi and S Rinzivillo}
}
@conference {507,
	title = {DEMON: a Local-First Discovery Method for Overlapping Communities},
	booktitle = {KDD 2012},
	year = {2012},
	month = {2012},
	author = {Michele Coscia and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {625,
	title = {DEMON: a local-first discovery method for overlapping communities},
	booktitle = {The 18th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, {KDD} {\textquoteright}12, Beijing, China, August 12-16, 2012},
	year = {2012},
	doi = {10.1145/2339530.2339630},
	url = {http://doi.acm.org/10.1145/2339530.2339630},
	author = {Michele Coscia and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@article {455,
	title = {Discovering the Geographical Borders of Human Mobility},
	journal = {KI - K{\"u}nstliche Intelligenz},
	year = {2012},
	chapter = {1},
	abstract = {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.},
	issn = {0933-1875},
	doi = {10.1007/s13218-012-0181-8},
	url = {https://link.springer.com/article/10.1007\%2Fs13218-012-0181-8},
	author = {S Rinzivillo and Simone Mainardi and Fabio Pezzoni and Michele Coscia and Fosca Giannotti and Dino Pedreschi}
}
@conference {sebd10,
	title = {Discovering Eras in Evolving Social Networks (Extended Abstract)},
	booktitle = {SEBD},
	year = {2010},
	pages = {78-85},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/O,
	title = {DAEDALUS: A knowledge discovery analysis framework for movement data},
	booktitle = {SEBD},
	year = {2008},
	pages = {191-198},
	author = {Riccardo Ortale and E Ritacco and N. Pelekisy and Roberto Trasarti and Gianni Costa and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Yannis Theodoridis}
}
@conference {RPTCGMRT08,
	title = {The DAEDALUS framework: progressive querying and mining of movement data},
	booktitle = {GIS},
	year = {2008},
	pages = {52},
	author = {Riccardo Ortale and E Ritacco and Nikos Pelekis and Roberto Trasarti and Gianni Costa and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Yannis Theodoridis}
}
@conference {DBLP:conf/dmkd/BacarellaGNP04,
	title = {Discovery of ads web hosts through traffic data analysis},
	booktitle = {DMKD},
	year = {2004},
	pages = {76-81},
	author = {V. Bacarella and Fosca Giannotti and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/itcc/BonchiGMRNPR01,
	title = {Data Mining for Intelligent Web Caching},
	booktitle = {ITCC},
	year = {2001},
	pages = {599-603},
	author = {Francesco Bonchi and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Mirco Nanni and Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/itcc/BonchiGMRNPR01,
	title = {Data Mining for Intelligent Web Caching},
	booktitle = {ITCC},
	year = {2001},
	pages = {599-603},
	author = {Francesco Bonchi and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Mirco Nanni and Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/fqas/GiannottiM00,
	title = {Declarative Knowledge Extraction with Interactive User-Defined Aggregates},
	booktitle = {FQAS},
	year = {2000},
	pages = {435-444},
	author = {Fosca Giannotti and Giuseppe Manco}
}
@article {DBLP:journals/jlp/GiannottiP98,
	title = {Datalog with Non-Deterministic Choice Computers NDB-PTIME},
	journal = {J. Log. Program.},
	volume = {35},
	number = {1},
	year = {1998},
	pages = {79-101},
	author = {Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/GiannottiMNP97,
	title = {Datalog++: a Basis for Active Object.Oriented Databases},
	booktitle = {SEBD},
	year = {1997},
	pages = {325-340},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/dood/GiannottiMNP97,
	title = {Datalog++: A Basis for Active Object-Oriented Databases},
	booktitle = {DOOD},
	year = {1997},
	pages = {283-301},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/agp/GiannottiMP97,
	title = {A Deductive Data Model for Representing and Querying Semistructured Data},
	booktitle = {APPIA-GULP-PRODE},
	year = {1997},
	pages = {129-140},
	author = {Fosca Giannotti and Giuseppe Manco and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/CarboniGFP95,
	title = {Declarative Reconstruction of Updates in Logic Databases: A Compilative Approach},
	booktitle = {SEBD},
	year = {1995},
	pages = {3-13},
	author = {Marilisa E. Carboni and Fosca Giannotti and V. Foddai and Dino Pedreschi}
}
@conference {DBLP:conf/agp/CarboniFGP95,
	title = {Declarative Reconstruction of Updates in Logic Databases: a Compilative Approach},
	booktitle = {GULP-PRODE},
	year = {1995},
	pages = {169-182},
	author = {Marilisa E. Carboni and V. Foddai and Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/vldb/AmatoGM93,
	title = {Data Sharing Analysis for a Database Programming Lanaguage via Abstract Interpretation},
	booktitle = {VLDB},
	year = {1993},
	pages = {405-415},
	author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto}
}
@conference {DBLP:conf/dood/CorciuloGP93,
	title = {Datalog with Non-Deterministic Choice Computes NDB-PTIME},
	booktitle = {DOOD},
	year = {1993},
	pages = {49-66},
	author = {Luca Corciulo and Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/lpnmr/GiannottiP90,
	title = {Declarative Semantics for Pruning Operators in Logic Programming},
	booktitle = {LPNMR},
	year = {1990},
	pages = {27-37},
	author = {Fosca Giannotti and Dino Pedreschi}
}
