@article {1532,
	title = {Mobility Constraints in Segregation Models},
	journal = {Scientific Reports},
	volume = {13},
	year = {2023},
	pages = {12087},
	abstract = {Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.},
	doi = {10.1038/s41598-023-38519-6},
	author = {Daniele Gambetta and Giovanni Mauro and Luca Pappalardo}
}
@conference {1518,
	title = {A Model-Agnostic Heuristics for Selective Classification},
	booktitle = {Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, 2023, Washington, DC, USA, February 7-14, 2023},
	year = {2023},
	publisher = {AAAI Press},
	organization = {AAAI Press},
	abstract = {Selective classification (also known as classification with reject option) conservatively extends a classifier with a selection function to determine whether or not a prediction should be accepted (i.e., trusted, used, deployed). This is a highly relevant issue in socially sensitive tasks, such as credit scoring. State-of-the-art approaches rely on Deep Neural Networks (DNNs) that train at the same time both the classifier and the selection function. These approaches are model-specific and computationally expensive. We propose a model-agnostic approach, as it can work with any base probabilistic binary classification algorithm, and it can be scalable to large tabular datasets if the base classifier is so. The proposed algorithm, called SCROSS, exploits a cross-fitting strategy and theoretical results for quantile estimation to build the selection function. Experiments on real-world data show that SCROSS improves over existing methods.},
	doi = {10.1609/aaai.v37i8.26133},
	url = {https://doi.org/10.1609/aaai.v37i8.26133},
	author = {Andrea Pugnana and Salvatore Ruggieri}
}
@article {1489,
	title = {Methods and tools for causal discovery and causal inference},
	journal = {WIREs Data Mining Knowl. Discov.},
	volume = {12},
	number = {2},
	year = {2022},
	abstract = {Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples.},
	author = {Ana Rita Nogueira and Andrea Pugnana and Salvatore Ruggieri and Dino Pedreschi and Jo{\~a}o Gama}
}
@conference {1552,
	title = {Monitoring Fairness in HOLDA},
	booktitle = {HHAI 2022: Augmenting Human Intellect - Proceedings of the First International Conference on Hybrid Human-Artificial Intelligence, Amsterdam, The Netherlands, 13-17 June 2022},
	year = {2022},
	publisher = {IOS Press},
	organization = {IOS Press},
	doi = {10.3233/FAIA220205},
	url = {https://doi.org/10.3233/FAIA220205},
	author = {Michele Fontana and Francesca Naretto and Anna Monreale and Fosca Giannotti},
	editor = {Stefan Schlobach and Mar{\'\i}a P{\'e}rez-Ortiz and Myrthe Tielman}
}
@article {1463,
	title = {A Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together},
	journal = {ISPRS International Journal of Geo-Information},
	volume = {10},
	number = {9},
	year = {2021},
	pages = {599},
	abstract = {Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.},
	issn = {2220-9964},
	doi = {10.3390/ijgi10090599},
	url = {https://www.mdpi.com/2220-9964/10/9/599},
	author = {Cornacchia, Giuliano and Luca Pappalardo}
}
@booklet {1425,
	title = {Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown},
	year = {2020},
	abstract = {Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modelling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. We address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?},
	doi = {https://dx.doi.org/10.32079/ISTI-TR-2020/005},
	url = {https://arxiv.org/abs/2004.11278},
	author = {Pietro Bonato and Paolo Cintia and Francesco Fabbri and Daniele Fadda and Fosca Giannotti and Pier Luigi Lopalco and Sara Mazzilli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Francesco Penone and S Rinzivillo and Giulio Rossetti and Marcello Savarese and Lara Tavoschi}
}
@article {1410,
	title = {Modeling Adversarial Behavior Against Mobility Data Privacy},
	journal = {IEEE Transactions on Intelligent Transportation SystemsIEEE Transactions on Intelligent Transportation Systems},
	year = {2020},
	month = {2020},
	pages = {1 - 14},
	abstract = {Privacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way.},
	isbn = {1558-0016},
	doi = {https://doi.org/10.1109/TITS.2020.3021911},
	url = {https://ieeexplore.ieee.org/abstract/document/9199893},
	author = {Roberto Pellungrini and Luca Pappalardo and F. Simini and Anna Monreale}
}
@article {1368,
	title = {Modelling Human Mobility considering Spatial, Temporal and Social Dimensions},
	journal = {arXiv preprint arXiv:2007.02371},
	year = {2020},
	author = {Cornacchia, Giuliano and Giulio Rossetti and Luca Pappalardo}
}
@conference {1215,
	title = {Meaningful explanations of Black Box AI decision systems},
	booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
	year = {2019},
	abstract = {Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user{\textquoteright}s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box.},
	doi = {10.1609/aaai.v33i01.33019780},
	url = {https://aaai.org/ojs/index.php/AAAI/article/view/5050},
	author = {Dino Pedreschi and Fosca Giannotti and Riccardo Guidotti and Anna Monreale and Salvatore Ruggieri and Franco Turini}
}
@conference {1024,
	title = {Market Basket Prediction using User-Centric Temporal Annotated Recurring Sequences},
	booktitle = {2017 IEEE International Conference on Data Mining (ICDM)},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer{\textquoteright}s decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern named Temporal Annotated Recurring Sequence (TARS). We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer{\textquoteright}s stocks and recommend the set of most necessary items. A deep experimentation shows that TARS can explain the customers{\textquoteright} purchase behavior, and that TBP outperforms the state-of-the-art competitors.},
	author = {Riccardo Guidotti and Giulio Rossetti and Luca Pappalardo and Fosca Giannotti and Dino Pedreschi}
}
@conference {1030,
	title = {Movement Behaviour Recognition for Water Activities},
	booktitle = {Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, {PAP} 2017, Held in Conjunction with {ECML} {PKDD} 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers},
	year = {2017},
	doi = {10.1007/978-3-319-71970-2_7},
	url = {https://doi.org/10.1007/978-3-319-71970-2_7},
	author = {Mirco Nanni and Roberto Trasarti and Fosca Giannotti}
}
@article {821,
	title = {MyWay: Location prediction via mobility profiling},
	journal = {Information Systems},
	volume = {64},
	year = {2017},
	month = {03/2017},
	pages = {350{\textendash}367},
	abstract = {Forecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user׳s movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.},
	author = {Roberto Trasarti and Riccardo Guidotti and Anna Monreale and Fosca Giannotti}
}
@conference {761,
	title = {Managing travels with PETRA: The Rome use case},
	booktitle = {2015 31st IEEE International Conference on Data Engineering Workshops (ICDEW)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The aim of the PETRA project is to provide the basis for a city-wide transportation system that supports policies catering for both individual preferences of users and city-wide travel patterns. The PETRA platform will be initially deployed in the partner city of Rome, and later in Venice, and Tel-Aviv.},
	author = {Botea, Adi and Braghin, Stefano and Lopes, Nuno and Riccardo Guidotti and Francesca Pratesi}
}
@conference {975,
	title = {Mining learning processes from FLOSS mailing archives},
	booktitle = {Conference on e-Business, e-Services and e-Society},
	year = {2015},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Evidence suggests that Free/Libre Open Source Software (FLOSS) environments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while making use of these environments. As FLOSS repositories store data about participants{\textquoteright} interaction and activities, we analyze participants{\textquoteright} interaction and knowledge exchange in emails to trace learning activities that occur in distinct phases of the learning process. We make use of semantic search in SQL to retrieve data and build corresponding event logs which are then fed to a process mining tool in order to produce visual workflow nets. We view these nets as representative of the traces of learning activities in FLOSS as well as their relevant flow of occurrence. Additional statistical details are provided to contextualize and describe these models.},
	doi = {10.1007/978-3-319-25013-7_23},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {764,
	title = {Mobility Mining for Journey Planning in Rome},
	booktitle = {Machine Learning and Knowledge Discovery in Databases},
	year = {2015},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {We present recent results on integrating private car GPS routines obtained by a Data Mining module. into the PETRA (PErsonal TRansport Advisor) platform. The routines are used as additional {\textquotedblleft}bus lines{\textquotedblright}, available to provide a ride to travelers. We present the effects of querying the planner with and without the routines, which show how Data Mining may help Smarter Cities applications.},
	author = {Michele Berlingerio and Bicer, Veli and Botea, Adi and Braghin, Stefano and Lopes, Nuno and Riccardo Guidotti and Francesca Pratesi}
}
@conference {icwe/LiraRTR14,
	title = {{MAPMOLTY:} {A} Web Tool for Discovering Place Loyalty Based on Mobile Crowdsource Data},
	booktitle = {Web Engineering, 14th International Conference, {ICWE} 2014, Toulouse, France, July 1-4, 2014. Proceedings},
	year = {2014},
	pages = {528{\textendash}531},
	doi = {10.1007/978-3-319-08245-5_43},
	url = {http://dx.doi.org/10.1007/978-3-319-08245-5_43},
	author = {Vinicius Monteiro de Lira and S Rinzivillo and Val{\'e}ria Ces{\'a}rio Times and Chiara Renso}
}
@conference {727,
	title = {Mining efficient training patterns of non-professional cyclists},
	booktitle = {22nd Italian Symposium on Advanced Database Systems, {SEBD} 2014, Sorrento Coast, Italy, June 16-18, 2014.},
	year = {2014},
	author = {Paolo Cintia and Luca Pappalardo and Dino Pedreschi}
}
@inbook {575,
	title = {Mobility Profiling},
	booktitle = {Data Science and Simulation in Transportation Research},
	year = {2014},
	pages = {1-29},
	publisher = {IGI Global},
	organization = {IGI Global},
	chapter = {1},
	abstract = {The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. },
	doi = {10.4018/978-1-4666-4920-0.ch001},
	author = {Mirco Nanni and Roberto Trasarti and Paolo Cintia and Barbara Furletti and Chiara Renso and Lorenzo Gabrielli and S Rinzivillo and Fosca Giannotti}
}
@article {982,
	title = {A multidisciplinary survey on discrimination analysis},
	journal = {The Knowledge Engineering Review},
	volume = {29},
	number = {5},
	year = {2014},
	pages = {582{\textendash}638},
	abstract = {The collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last 50 years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a multidisciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, and computational issues. The objective of this survey is to provide a guidance and a glue for researchers and anti-discrimination data analysts on concepts, problems, application areas, datasets, methods, and approaches from a multidisciplinary perspective. We organize the approaches according to their method of data collection as observational, quasi-experimental, and experimental studies. A fourth line of recently blooming research on knowledge discovery based methods is also covered. Observational methods are further categorized on the basis of their application context: labor economics, social profiling, consumer markets, and others.},
	doi = {10.1017/S0269888913000039},
	author = {Andrea Romei and Salvatore Ruggieri}
}
@conference {504,
	title = {Measuring tie strength in multidimensional networks},
	booktitle = {SEDB 2013},
	year = {2013},
	month = {2013},
	author = {Giulio Rossetti and Luca Pappalardo and Dino Pedreschi}
}
@mastersthesis {643,
	title = {Mobility Ranking - Human Mobility Analysis Using Ranking Measures},
	year = {2013},
	author = {Riccardo Guidotti}
}
@conference {539,
	title = {Mob-Warehouse: A semantic approach for mobility analysis with a Trajectory Data Ware- house},
	booktitle = {SecoGIS 2013 - International Workshop on Semantic Aspects of GIS, Joint to ER conference 2013},
	year = {2013},
	address = {Hong Kong},
	author = {Ricardo Wagner and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Alessandra Raffaet{\`a} and Chiara Renso and Alessandro Roncato and Roberto Trasarti}
}
@conference {704,
	title = {MP4-A Project: Mobility Planning For Africa},
	booktitle = {In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013)},
	year = {2013},
	address = {Cambridge, USA},
	abstract = {This project aims to create a tool that uses mobile phone transaction (trajectory) data that will be able to address transportation related challenges, thus allowing promotion and facilitation of sustainable urban mobility planning in Third World countries. The proposed tool is a transport demand model for Ivory Coast, with emphasis on its major urbanization Abidjan. The consortium will bring together available data from the internet, and integrate these with the mobility data obtained from the mobile phones in order to build the best possible transport model. A transport model allows an understanding of current and future infrastructure requirements in Ivory Coast. As such, this project will provide the first proof of concept. In this context, long-term analysis of individual call traces will be performed to reconstruct systematic movements, and to infer an origin-destination matrix. A similar process will be performed using the locations of caller and recipient of phone calls, enabling the comparison of socio-economic ties vs. mobility. The emerging links between different areas will be used to build an effective map to optimize regional border definitions and road infrastructure from a mobility perspective. Finally, we will try to build specialized origin-destination matrices for specific categories of population. Such categories will be inferred from data through analysis of calling behaviours, and will also be used to characterize the population of different cities. The project also includes a study of data compliance with distributions of standard measures observed in literature, including distribution of calls, call durations and call network features.},
	url = {http://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf},
	author = {Mirco Nanni and Roberto Trasarti and Barbara Furletti and Lorenzo Gabrielli and Peter Van Der Mede and Joost De Bruijn and Erik de Romph and Gerard Bruil}
}
@conference {505,
	title = {On multidimensional network measures},
	booktitle = {SEDB 2013},
	year = {2013},
	month = {2013},
	abstract = {Networks, i.e., sets of interconnected entities, are ubiquitous,
spanning disciplines as diverse as sociology, biology and computer science.
The recent availability of large amounts of network data has thus
provided a unique opportunity to develop models and analysis tools applicable
to a wide range of scenarios. However, real-world phenomena are
often more complex than existing graph data models. One relevant example
concerns the numerous types of social relationships (or edges) that
can be present between individuals in a social network. In this short paper
we present a unified model and a set of measures recently developed
to represent and analyze network data with multiple types of edges.},
	url = {https://www.researchgate.net/publication/256194479_On_multidimensional_network_measures},
	author = {Matteo Magnani and Anna Monreale and Giulio Rossetti and Fosca Giannotti}
}
@conference {544,
	title = {M-Attract: Assessing Places Attractiveness by using Moving Objects Trajectories Data},
	booktitle = {GEOINFO 2012 Brazilian Conference on Geographical Information Systems},
	year = {2012},
	month = {2012},
	author = {Andr{\'e} Salvaro Furtado and Renato Fileto and Chiara Renso}
}
@conference {685,
	title = {Mega-modeling for Big Data Analytics},
	booktitle = {Conceptual Modeling - 31st International Conference {ER} 2012, Florence, Italy, October 15-18, 2012. Proceedings},
	year = {2012},
	doi = {10.1007/978-3-642-34002-4_1},
	url = {http://dx.doi.org/10.1007/978-3-642-34002-4_1},
	author = {Stefano Ceri and Emanuele Della Valle and Dino Pedreschi and Roberto Trasarti}
}
@article {482,
	title = {Multidimensional networks: foundations of structural analysis},
	journal = {World Wide Web},
	volume = { Volume 15 / 2012},
	year = {2012},
	month = {10/2012},
	abstract = {Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. So far, network analysis has focused on the characterization and measurement of local and global properties of graphs, such as diameter, degree distribution, centrality, and so on. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens in monodimensional networks, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. We tested our framework on different real world multidimensional networks, showing the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks. },
	doi = {10.1007/s11280-012-0190-4},
	url = {http://www.springerlink.com/content/f774289854430410/abstract/},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@article {1274,
	title = {Measuring the effectiveness of homeopathic care through objective and shared indicators},
	journal = {Homeopathy},
	volume = {100},
	number = {04},
	year = {2011},
	pages = {212{\textendash}219},
	author = {Leone, Laura and Marchitiello, Maria and Michela Natilli and Romano, Maria Francesca}
}
@conference {474,
	title = {Mining Influence Rules out of Ontologies},
	booktitle = {International Conference on Software and Data Technologies (ICSOFT)},
	year = {2011},
	month = {2011},
	address = {Siviglia, Spagna},
	author = {Barbara Furletti and Franco Turini}
}
@conference {TrasartiPNG11,
	title = {Mining mobility user profiles for car pooling},
	booktitle = {KDD},
	year = {2011},
	pages = {1190-1198},
	author = {Roberto Trasarti and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {GiannottiNPPR10,
	title = {Mobility data mining: discovering movement patterns from trajectory data},
	booktitle = {Computational Transportation Science},
	year = {2010},
	pages = {7-10},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@article {572,
	title = {Movement Data Anonymity through Generalization},
	journal = {Transactions on Data Privacy},
	volume = {3},
	number = {2},
	year = {2010},
	pages = {91{\textendash}121},
	abstract = {Wireless networks and mobile devices, such as mobile phones and GPS receivers, sense
and track the movements of people and vehicles, producing society-wide mobility databases. This is
a challenging scenario for data analysis and mining. On the one hand, exciting opportunities arise out
of discovering new knowledge about human mobile behavior, and thus fuel intelligent info-mobility
applications. On other hand, new privacy concerns arise when mobility data are published. The
risk is particularly high for GPS trajectories, which represent movement of a very high precision and
spatio-temporal resolution: the de-identification of such trajectories (i.e., forgetting the ID of their
associated owners) is only a weak protection, as generally it is possible to re-identify a person by observing
her routine movements. In this paper we propose a method for achieving true anonymity in
a dataset of published trajectories, by defining a transformation of the original GPS trajectories based
on spatial generalization and k-anonymity. The proposed method offers a formal data protection
safeguard, quantified as a theoretical upper bound to the probability of re-identification. We conduct
a thorough study on a real-life GPS trajectory dataset, and provide strong empirical evidence that
the proposed anonymity techniques achieve the conflicting goals of data utility and data privacy. In
practice, the achieved anonymity protection is much stronger than the theoretical worst case, while
the quality of the cluster analysis on the trajectory data is preserved.},
	url = {http://www.tdp.cat/issues/abs.a045a10.php},
	author = {Anna Monreale and Gennady Andrienko and Natalia Andrienko and Fosca Giannotti and Dino Pedreschi and S Rinzivillo and Stefan Wrobel}
}
@conference {sdmPedreschiRT09,
	title = {Measuring Discrimination in Socially-Sensitive Decision Records},
	booktitle = {SDM},
	year = {2009},
	pages = {581-592},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@inbook {BerlingerioBCGT9,
	title = {Mining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation},
	booktitle = {Biomedical Data and Applications},
	year = {2009},
	pages = {211-236},
	author = {Michele Berlingerio and Francesco Bonchi and Michele Curcio and Fosca Giannotti and Franco Turini}
}
@conference {BerlingerioBBG09,
	title = {Mining Graph Evolution Rules},
	booktitle = {ECML/PKDD 2009},
	year = {2009},
	pages = {115-130},
	address = {Bled, Slovenia},
	author = {Michele Berlingerio and Francesco Bonchi and Bj{\"o}rn Bringmann and Aristides Gionis}
}
@conference {GiannottiNPRT09,
	title = {Mining Mobility Behavior from Trajectory Data},
	booktitle = {CSE (4)},
	year = {2009},
	pages = {948-951},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso and Roberto Trasarti}
}
@conference {sebd09,
	title = {Mining the Information Propagation in a Network},
	booktitle = {SEBD},
	year = {2009},
	pages = {333-340},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {BerlingerioCG09,
	title = {Mining the Information Propagation in a Network},
	booktitle = {SEBD},
	year = {2009},
	pages = {333-340},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {BerlingerioCG09,
	title = {Mining the Temporal Dimension of the Information Propagation},
	booktitle = {IDA},
	year = {2009},
	pages = {237-248},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {ida09,
	title = {Mining the Temporal Dimension of the Information Propagation},
	booktitle = {IDA},
	year = {2009},
	pages = {237-248},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {991,
	title = {Movement data anonymity through generalization},
	booktitle = {Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS},
	year = {2009},
	publisher = {ACM},
	organization = {ACM},
	abstract = {In recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the diffusion of mobile devices (e.g., mobile phones, RFID devices and GPS devices) and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Clearly, in these applications privacy is a concern, since models extracted from this kind of data can reveal the behavior of group of individuals, thus compromising their privacy. Movement data present a new challenge for the privacy-preserving data mining community because of their spatial and temporal characteristics.

In this position paper we briefly present an approach for the generalization of movement data that can be adopted for obtaining k-anonymity in spatio-temporal datasets; specifically, it can be used to realize a framework for publishing of spatio-temporal data while preserving privacy. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.},
	doi = {10.1145/1667502.1667510},
	author = {Gennady Andrienko and Natalia Andrienko and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@inbook {GiannottiP08,
	title = {Mobility, Data Mining and Privacy: A Vision of Convergence},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {1-11},
	author = {Fosca Giannotti and Dino Pedreschi}
}
@book {2008mdmp,
	title = {Mobility, Data Mining and Privacy - Geographic Knowledge Discovery},
	series = {Mobility, Data Mining and Privacy},
	year = {2008},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-540-75176-2},
	author = {Fosca Giannotti and Dino Pedreschi},
	editor = {Fosca Giannotti and Dino Pedreschi}
}
@conference {GiannottiPT08,
	title = {Mobility, Data Mining and Privacy the Experience of the GeoPKDD Project},
	booktitle = {PinKDD},
	year = {2008},
	pages = {25-32},
	author = {Fosca Giannotti and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/bibm/BerlingerioBGT07,
	title = {Mining Clinical Data with a Temporal Dimension: A Case Study},
	booktitle = {BIBM},
	year = {2007},
	pages = {429-436},
	author = {Michele Berlingerio and Francesco Bonchi and Fosca Giannotti and Franco Turini}
}
@inbook {flexible,
	title = {Maximum Entropy Reasoning for GIS},
	year = {2006},
	note = {Flexible Databases supporting Imprecision and Uncertainty, Physica Verlag, June},
	author = {H. Hosni and Maria V Masserotti and Chiara Renso}
}
@conference {DBLP:conf/cbms/BerlingerioBCCGS06,
	title = {Mining HLA Patterns Explaining Liver Diseases},
	booktitle = {CBMS},
	year = {2006},
	pages = {702-707},
	author = {Michele Berlingerio and Francesco Bonchi and Silvia Chelazzi and Michele Curcio and Fosca Giannotti and Fabrizio Scatena}
}
@conference {DBLP:conf/sac/GiannottiNPP06,
	title = {Mining sequences with temporal annotations},
	booktitle = {SAC},
	year = {2006},
	pages = {593-597},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli}
}
@proceedings {DBLP:conf/ecml/2004,
	title = {Machine Learning: ECML 2004, 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings},
	volume = {3201},
	year = {2004},
	publisher = {Springer},
	isbn = {3-540-23105-6},
	author = {Jean-Fran{\c c}ois Boulicaut and Floriana Esposito and Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/pakdd/GiannottiM00,
	title = {Making Knowledge Extraction and Reasoning Closer},
	booktitle = {PAKDD},
	year = {2000},
	pages = {360-371},
	author = {Fosca Giannotti and Giuseppe Manco}
}
@book {Ren98,
	title = {Mechanisms for Semantic Integration of Deductive Databases},
	year = {1998},
	note = {PhD thesis, Dipartimento di Informatica, University of Pisa, .},
	author = {Chiara Renso}
}
@article {RR98,
	title = {A Mediator Approach for Representing Knowledge},
	journal = {Intelligent Multimedia Presentation Systems. Human Computer Interaction Letters, 1 (1): 32-38, April 1998.},
	year = {1998},
	author = {Chiara Renso and Salvatore Ruggieri}
}
@article {DBLP:journals/toplas/BrogiMPT94,
	title = {Modular Logic Programming},
	journal = {ACM Trans. Program. Lang. Syst.},
	volume = {16},
	number = {4},
	year = {1994},
	pages = {1361-1398},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/meta/BrogiMPT92,
	title = {Meta for Modularising Logic Programming},
	booktitle = {META},
	year = {1992},
	pages = {105-119},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
