@article {564,
	title = {Anonymity preserving sequential pattern mining},
	journal = {Artif. Intell. Law},
	volume = {22},
	number = {2},
	year = {2014},
	pages = {141{\textendash}173},
	abstract = {The increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility.},
	doi = {10.1007/s10506-014-9154-6},
	url = {http://dx.doi.org/10.1007/s10506-014-9154-6},
	author = {Anna Monreale and Dino Pedreschi and Ruggero G. Pensa and Fabio Pinelli}
}
@conference {686,
	title = {Individual Mobility Profiles: Methods and Application on Vehicle Sharing},
	booktitle = {Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings},
	year = {2012},
	url = {http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb},
	author = {Roberto Trasarti and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@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 {OngPTNRRG11,
	title = {Traffic Jams Detection Using Flock Mining},
	booktitle = {ECML/PKDD (3)},
	year = {2011},
	pages = {650-653},
	author = {Rebecca Ong and Fabio Pinelli and Roberto Trasarti and Mirco Nanni and Chiara Renso and S Rinzivillo and Fosca Giannotti}
}
@article {vlbdjMatlas,
	title = {Unveiling the complexity of human mobility by querying and mining massive trajectory data},
	journal = {VLDB J.},
	volume = {20},
	number = {5},
	year = {2011},
	pages = {695-719},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {TrasartiRPNM10,
	title = {Exploring Real Mobility Data with M-Atlas},
	booktitle = {ECML/PKDD (3)},
	year = {2010},
	pages = {624-627},
	abstract = {Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applications.},
	doi = {10.1007/978-3-642-15939-8_48},
	author = {Roberto Trasarti and S Rinzivillo and Fabio Pinelli and Mirco Nanni and Anna Monreale and Chiara Renso and Dino Pedreschi and Fosca Giannotti}
}
@conference {MonrealePTG10,
	title = {Location Prediction through Trajectory Pattern Mining (Extended Abstract)},
	booktitle = {SEBD},
	year = {2010},
	pages = {134-141},
	author = {Anna Monreale and Fabio Pinelli and Roberto Trasarti 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}
}
@proceedings {242,
	title = {Anonymous Sequences from Trajectory Data},
	year = {2009},
	edition = {17},
	address = {Camogli, Italy},
	author = {Ruggero G. Pensa and Anna Monreale and Fabio Pinelli and Dino Pedreschi}
}
@conference {BerlingerioPNG09,
	title = {Temporal mining for interactive workflow data analysis},
	booktitle = {KDD},
	year = {2009},
	pages = {109-118},
	author = {Michele Berlingerio and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {DBLP:conf/gis/Gi,
	title = {Trajectory pattern analysis for urban traffic},
	booktitle = {Second International Workshop on Computational Transportation Science},
	year = {2009},
	month = {11/2009},
	pages = {43-47},
	publisher = {ACM},
	organization = {ACM},
	address = {SEATTLE, USA},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli}
}
@proceedings {243,
	title = {WhereNext: a Location Predictor on Trajectory Pattern Mining},
	year = {2009},
	abstract = {The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.},
	doi = {10.1145/1557019.1557091},
	author = {Anna Monreale and Fabio Pinelli and Roberto Trasarti and Fosca Giannotti}
}
@proceedings {241,
	title = {Location prediction within the mobility data analysis environment Daedalus},
	year = {2008},
	address = {Dublin, Ireland},
	abstract = {In this paper we propose a method to predict the next location of a moving object based on two recent results in GeoPKDD project: DAEDALUS, a mobility data analysis environment and Trajectory Pattern, a sequential pattern mining algorithm with temporal annotation integrated in DAEDALUS. The first one is a DMQL environment for moving objects, where both data and patterns can be represented. The second one extracts movement patterns as sequences of movements between locations with typical travel times.

This paper proposes a prediction method which uses the local models extracted by Trajectory Pattern to build a global model called Prediction Tree. The future location of a moving object is predicted visiting the tree and calculating the best matching function.

The integration within DAEDALUS system supports an interactive construction of the predictor on the top of a set of spatio-temporal patterns.

Others proposals in literature base the definition of prediction methods for future location of a moving object on previously extracted frequent patterns. They use the recent history of movements of the object itself and often use time only to order the events. Our work uses the movements of all moving objects in a certain area to learn a classifier built on the mined trajectory patterns, which are intrinsically equipped with temporal information.},
	doi = {10.4108/ICST.MOBIQUITOUS2008.3894},
	author = {Fabio Pinelli and Anna Monreale and Roberto Trasarti and Fosca Giannotti}
}
@conference {DBLP:conf/esoric,
	title = {Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining},
	booktitle = {PiLBA},
	year = {2008},
	abstract = {Sequential pattern mining is a major research field in knowledge
discovery and data mining. Thanks to the increasing availability of
transaction data, it is now possible to provide new and improved services
based on users{\textquoteright} and customers{\textquoteright} behavior. However, this puts the citizen{\textquoteright}s
privacy at risk. Thus, it is important to develop new privacy-preserving
data mining techniques that do not alter the analysis results significantly.
In this paper we propose a new approach for anonymizing sequential
data by hiding infrequent, and thus potentially sensible, subsequences.
Our approach guarantees that the disclosed data are k-anonymous and
preserve the quality of extracted patterns. An application to a real-world
moving object database is presented, which shows the effectiveness of our
approach also in complex contexts.},
	url = {https://air.unimi.it/retrieve/handle/2434/52786/106397/ProceedingsPiLBA08.pdf$\#$page=44},
	author = {Ruggero G. Pensa and Anna Monreale and Fabio Pinelli and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/BerlingerioGNP08,
	title = {Temporal analysis of process logs: a case study},
	booktitle = {SEBD},
	year = {2008},
	pages = {430-437},
	author = {Michele Berlingerio and Fosca Giannotti and Mirco Nanni and Fabio Pinelli}
}
@conference {DBLP:conf/kdd/GiannottiNPP07,
	title = {Trajectory pattern mining},
	booktitle = {KDD},
	year = {2007},
	pages = {330-339},
	author = {Fosca Giannotti and Mirco Nanni and Fabio Pinelli and Dino Pedreschi}
}
@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}
}
