TY - JOUR T1 - Anonymity preserving sequential pattern mining JF - Artif. Intell. Law Y1 - 2014 A1 - Anna Monreale A1 - Dino Pedreschi A1 - Ruggero G. Pensa A1 - Fabio Pinelli AB - 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. VL - 22 UR - http://dx.doi.org/10.1007/s10506-014-9154-6 ER - TY - CONF T1 - Individual Mobility Profiles: Methods and Application on Vehicle Sharing T2 - Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings Y1 - 2012 A1 - Roberto Trasarti A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Fosca Giannotti JF - Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings UR - http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb ER - TY - CONF T1 - Mining mobility user profiles for car pooling T2 - KDD Y1 - 2011 A1 - Roberto Trasarti A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Fosca Giannotti JF - KDD ER - TY - CONF T1 - Traffic Jams Detection Using Flock Mining T2 - ECML/PKDD (3) Y1 - 2011 A1 - Rebecca Ong A1 - Fabio Pinelli A1 - Roberto Trasarti A1 - Mirco Nanni A1 - Chiara Renso A1 - S Rinzivillo A1 - Fosca Giannotti JF - ECML/PKDD (3) ER - TY - JOUR T1 - Unveiling the complexity of human mobility by querying and mining massive trajectory data JF - VLDB J. Y1 - 2011 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti VL - 20 ER - TY - CONF T1 - Exploring Real Mobility Data with M-Atlas T2 - ECML/PKDD (3) Y1 - 2010 A1 - Roberto Trasarti A1 - S Rinzivillo A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Anna Monreale A1 - Chiara Renso A1 - Dino Pedreschi A1 - Fosca Giannotti AB - 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. JF - ECML/PKDD (3) ER - TY - CONF T1 - Location Prediction through Trajectory Pattern Mining (Extended Abstract) T2 - SEBD Y1 - 2010 A1 - Anna Monreale A1 - Fabio Pinelli A1 - Roberto Trasarti A1 - Fosca Giannotti JF - SEBD ER - TY - CONF T1 - Mobility data mining: discovering movement patterns from trajectory data T2 - Computational Transportation Science Y1 - 2010 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti JF - Computational Transportation Science ER - TY - Generic T1 - Anonymous Sequences from Trajectory Data T2 - 17th Italian Symposium on Advanced Database Systems Y1 - 2009 A1 - Ruggero G. Pensa A1 - Anna Monreale A1 - Fabio Pinelli A1 - Dino Pedreschi JF - 17th Italian Symposium on Advanced Database Systems CY - Camogli, Italy ER - TY - CONF T1 - Temporal mining for interactive workflow data analysis T2 - KDD Y1 - 2009 A1 - Michele Berlingerio A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Fosca Giannotti JF - KDD ER - TY - CONF T1 - Trajectory pattern analysis for urban traffic T2 - Second International Workshop on Computational Transportation Science Y1 - 2009 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli JF - Second International Workshop on Computational Transportation Science PB - ACM CY - SEATTLE, USA ER - TY - Generic T1 - WhereNext: a Location Predictor on Trajectory Pattern Mining T2 - 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Y1 - 2009 A1 - Anna Monreale A1 - Fabio Pinelli A1 - Roberto Trasarti A1 - Fosca Giannotti AB - 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. JF - 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining ER - TY - Generic T1 - Location prediction within the mobility data analysis environment Daedalus T2 - First International Workshop on Computational Transportation Science Y1 - 2008 A1 - Fabio Pinelli A1 - Anna Monreale A1 - Roberto Trasarti A1 - Fosca Giannotti AB - 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. JF - First International Workshop on Computational Transportation Science CY - Dublin, Ireland ER - TY - CONF T1 - Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining T2 - PiLBA Y1 - 2008 A1 - Ruggero G. Pensa A1 - Anna Monreale A1 - Fabio Pinelli A1 - Dino Pedreschi AB - 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’ and customers’ behavior. However, this puts the citizen’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. JF - PiLBA UR - https://air.unimi.it/retrieve/handle/2434/52786/106397/ProceedingsPiLBA08.pdf#page=44 ER - TY - CONF T1 - Temporal analysis of process logs: a case study T2 - SEBD Y1 - 2008 A1 - Michele Berlingerio A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Fabio Pinelli JF - SEBD ER - TY - CONF T1 - Trajectory pattern mining T2 - KDD Y1 - 2007 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Fabio Pinelli A1 - Dino Pedreschi JF - KDD ER - TY - CONF T1 - Mining sequences with temporal annotations T2 - SAC Y1 - 2006 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli JF - SAC ER -