@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 {1285,
	title = {Doctor XAI: an ontology-based approach to black-box sequential data classification explanations},
	booktitle = {Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency},
	year = {2020},
	abstract = {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.},
	doi = {10.1145/3351095.3372855},
	url = {https://dl.acm.org/doi/pdf/10.1145/3351095.3372855?download=true},
	author = {Cecilia Panigutti and Perotti, Alan and Dino Pedreschi}
}
@article {1265,
	title = {Defining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data},
	journal = {Available at SSRN 3452058},
	year = {2019},
	author = {Bruestle, Stephen and Luca Pappalardo and Riccardo Guidotti}
}
@article {1297,
	title = {Do {\textquotedblleft}girls just wanna have fun{\textquotedblright}? Participation trends and motivational profiles of women in Norway{\textquoteright}s ultimate mass participation ski event},
	journal = {Frontiers in Psychology},
	volume = {10},
	year = {2019},
	pages = {2548},
	abstract = {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{\textquoteright}s participation in this specific MPSE, as well as add to the understanding of women{\textquoteright}s MPSEs participation in general, this study was set up to: (i) analyze trends in women{\textquoteright}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{\textendash}2018 were analyzed using an autoregressive model. Information on women{\textquoteright}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{\textquoteright}s participation in the overall event, a future increment of women in the main BR was predicted, with women{\textquoteright}s ratings possibly matching the men{\textquoteright}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. {\textquotedblleft}Exercise goal{\textquotedblright} 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{\textquoteright}s participation in MPSEs.},
	doi = {10.3389/fpsyg.2019.02548},
	url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02548/full},
	author = {Calogiuri, Giovanna and Johansen, Patrick Foss and Alessio Rossi and Thurston, Miranda}
}
@article {1377,
	title = {DynComm R Package{\textendash}Dynamic Community Detection for Evolving Networks},
	journal = {arXiv preprint arXiv:1905.01498},
	year = {2019},
	author = {Sarmento, Rui Portocarrero and Lemos, Lu{\'\i}s and Cordeiro, M{\'a}rio and Giulio Rossetti and Cardoso, Douglas}
}
@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 {1013,
	title = {A Data Mining Approach to Assess Privacy Risk in Human Mobility Data},
	journal = {ACM Trans. Intell. Syst. Technol.},
	volume = {9},
	number = {3},
	year = {2017},
	pages = {31:1{\textendash}31:27},
	abstract = {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.},
	issn = {2157-6904},
	doi = {10.1145/3106774},
	url = {http://doi.acm.org/10.1145/3106774},
	author = {Roberto Pellungrini and Luca Pappalardo and Francesca Pratesi and Anna Monreale}
}
@unpublished {1512,
	title = {Data Science a Game-changer for Science and Innovation},
	year = {2017},
	month = {03/2017},
	publisher = {G7 Academy},
	abstract = {Digital technology is ubiquitous and very much part of public and private organizations and of individuals{\textquoteright} 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.},
	author = {Fabio Beltram and Fosca Giannotti and Dino Pedreschi}
}
@article {1023,
	title = {Data-driven generation of spatio-temporal routines in human mobility},
	journal = {Data Mining and Knowledge Discovery},
	year = {2017},
	month = {Dec},
	abstract = {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{\textquoteright} 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.},
	issn = {1573-756X},
	doi = {10.1007/s10618-017-0548-4},
	url = {https://doi.org/10.1007/s10618-017-0548-4},
	author = {Luca Pappalardo and Filippo Simini}
}
@article {1037,
	title = {Discovering and Understanding City Events with Big Data: The Case of Rome},
	journal = {Information},
	volume = {8},
	number = {3},
	year = {2017},
	month = {06/2017},
	pages = {74},
	abstract = {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.},
	doi = {10.3390/info8030074},
	url = {https://doi.org/10.3390/info8030074},
	author = {Barbara Furletti and Roberto Trasarti and Paolo Cintia and Lorenzo Gabrielli}
}
@conference {1390,
	title = {Dynamic community analysis in decentralized online social networks},
	booktitle = {European Conference on Parallel Processing},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	author = {Guidi, Barbara and Michienzi, Andrea and Giulio Rossetti}
}
@booklet {963,
	title = {Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach.},
	year = {2016},
	abstract = {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 {\textquotedblleft}Inductive Constraint Programming{\textquotedblright} 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. },
	doi = {10.1007/978-3-319-50137-6},
	author = {Bessiere, Christian and De Raedt, Luc and Lars Kotthoff and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Dino Pedreschi}
}
@inbook {965,
	title = {Data Mining and Constraints: An Overview},
	booktitle = {Data Mining and Constraint Programming},
	year = {2016},
	pages = {25{\textendash}48},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {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.},
	doi = {10.1007/978-3-319-50137-6_2},
	author = {Valerio Grossi and Dino Pedreschi and Franco Turini}
}
@article {875,
	title = {Driving Profiles Computation and Monitoring for Car Insurance CRM},
	journal = {Journal ACM Transactions on Intelligent Systems and Technology (TIST)},
	volume = {8},
	number = {1},
	year = {2016},
	pages = {14:1{\textendash}14:26},
	abstract = {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.},
	doi = {10.1145/2912148},
	url = {http://doi.acm.org/10.1145/2912148},
	author = {Mirco Nanni and Roberto Trasarti and Anna Monreale and Valerio Grossi and Dino Pedreschi}
}
@article {804,
	title = {Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks},
	journal = {Microarrays},
	volume = {4},
	number = {2},
	year = {2015},
	pages = {255{\textendash}269},
	doi = {10.3390/microarrays4020255},
	url = {http://www.mdpi.com/2076-3905/4/2/255},
	author = {Alina Sirbu and Martin Crane and Heather J Ruskin}
}
@conference {689,
	title = {Detecting and understanding big events in big cities},
	booktitle = {NetMob},
	year = {2015},
	month = {04/2015},
	address = {Boston},
	abstract = {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{\textquoteright} 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.},
	url = {http://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf},
	author = {Barbara Furletti and Lorenzo Gabrielli and Roberto Trasarti and Zbigniew Smoreda and Maarten Vanhoof and Cezary Ziemlicki}
}
@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 {987,
	title = {Decision tree building on multi-core using FastFlow},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {26},
	number = {3},
	year = {2014},
	pages = {800{\textendash}820},
	abstract = {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 {\textcopyright} 2013 John Wiley \& Sons, Ltd.},
	doi = {10.1002/cpe.3063},
	author = {Aldinucci, Marco and Salvatore Ruggieri and Torquati, Massimo}
}
@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}
}
@conference {631,
	title = {Data Anonymity Meets Non-discrimination},
	booktitle = {Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on},
	year = {2013},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Salvatore Ruggieri}
}
@inbook {634,
	title = {The discovery of discrimination},
	booktitle = {Discrimination and privacy in the information society},
	year = {2013},
	pages = {91{\textendash}108},
	publisher = {Springer},
	organization = {Springer},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@article {632,
	title = {Discrimination discovery in scientific project evaluation: A case study},
	journal = {Expert Systems with Applications},
	volume = {40},
	number = {15},
	year = {2013},
	pages = {6064{\textendash}6079},
	author = {Andrea Romei and Salvatore Ruggieri and Franco Turini}
}
@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}
}
@book {1275,
	title = {Dinamiche di impoverimento. Meccanismi, traiettorie ed effetti in un contesto locale},
	year = {2011},
	publisher = {Carocci Editore},
	organization = {Carocci Editore},
	author = {Tomei, Gabriele and Michela Natilli}
}
@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 {TrasartiBR09,
	title = {DAMSEL: A System for Progressive Querying and Reasoning on Movement Data},
	booktitle = {DEXA Workshops},
	year = {2009},
	pages = {452-456},
	author = {Roberto Trasarti and Miriam Baglioni and Chiara Renso}
}
@article {bookdaniel,
	title = {Developing a Spatial Knowledge Representation for Pedestrian Interactions},
	year = {2009},
	note = {MOVEMENT-AWARE APPLICATIONS FOR SUSTAINABLE MOBILITY: TECHNOLOGIES AND APPROACHES, Monica Wachowicz Editor, IGI Publisher, To appear \subsection{Conferenze e Workshop}},
	author = {Daniel Ornellana, Chiara Renso}
}
@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}
}
@inbook {472,
	title = {Discovering Strategic Behaviour in Multi- Agent Scenarios by Ontology-Driven Mining},
	booktitle = {Advances in Robotics, Automation and Control},
	year = {2008},
	isbn = {978-953-7619-16-9},
	url = {http://www.intechopen.com/books/advances_in_robotics_automation_and_control/discovering_strategic_behaviors_in_multi-agent_scenarios_by_ontology-driven_mining},
	author = {Davide Bacciu and Andrea Bellandi and Barbara Furletti and Valerio Grossi and Andrea Romei}
}
@conference {PedreschiRT08,
	title = {Discrimination-aware data mining},
	booktitle = {KDD},
	year = {2008},
	pages = {560-568},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@conference {464,
	title = {DrC4.5: Improving C4.5 by means of Prior Knowledge},
	booktitle = {ACM Symposium on Applied Computing},
	year = {2005},
	publisher = {ACM},
	organization = {ACM},
	address = {Santa Fe, New Mexico, USA},
	isbn = {1-58113-964-0},
	doi = {http://dx.doi.org/10.1145/1066677.1066787},
	url = {http://dl.acm.org/ft_gateway.cfm?id=1066787\&ftid=311609\&dwn=1\&CFID=96873366\&CFTOKEN=59233511},
	author = {Miriam Baglioni and Barbara Furletti and Franco Turini}
}
@conference {DBLP:conf/wlp/NanniRRT04,
	title = {Deductive and Inductive Reasoning on Spatio-Temporal Data},
	booktitle = {INAP/WLP},
	year = {2004},
	pages = {98-115},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/sebd/NanniRRT04,
	title = {Deductive and Inductive Reasoning on Trajectories},
	booktitle = {SEBD},
	year = {2004},
	pages = {98-105},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@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/birthday/MascellaniP02,
	title = {The Declarative Side of Magic},
	booktitle = {Computational Logic: Logic Programming and Beyond},
	year = {2002},
	pages = {83-108},
	author = {Paolo Mascellani 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 {BRT99,
	title = {Dynamic Composition of Parameterised Logic Modules},
	journal = {Computer Languages},
	year = {1999},
	note = {25(4):.},
	pages = {211{\textendash}242},
	author = {Antonio Brogi and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/cl/BrogiRT99,
	title = {Dynamic composition of parameterised logic modules},
	journal = {Comput. Lang.},
	volume = {25},
	number = {4},
	year = {1999},
	pages = {211-242},
	author = {Antonio Brogi and Chiara Renso and Franco Turini}
}
@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/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/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/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/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/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/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}
}
