Abstract: Recent improvements in positioning technology have led to a much wider availability of massive moving object data. A crucial task is to find the moving objects such as people, animals, vehicles that travel together. In common, these object sets are called object movement patterns. Many approaches have been proposed to process and mine trajectory data. In this presentation I will first remind the concepts related to pattern mining followed by a survey on the major research in trajectory mining. Then I will show how the well-known itemset context can be very useful to extract different kinds of patterns. Finally I will show that these algorithms can be applied in different domains even if the spatial component is not present.
Resume: Pascal Poncelet is a full professor at the University of Montpellier, France, and head of the data mining research group (Advanse – Advanced Analytics for Data Science) at the Montpellier Laboratory of Informatics, Robotics and Microelectronics (LIRMM). He has previously worked as lecturer (1993-1994), as associate professor respectively in the Mediterannée University (1994-1999) and Montpellier University (1999-2001), as Professor at the Ecole des Mines d’Alès in France where he was also head of the KDD (Knowledge Discovery for Decision Making) team and co-head of the Computer Science Department (2001-2008). He received a PhD in computer science from the University of Nice-Sophia Antipolis. His research interests include advanced data analysis techniques (data mining, pattern mining, visual analytics, …) for emerging applications mainly dedicated to health and environment. He has published more than 150 papers in refereed international conferences and journals, has been in the program committees of many conferences and he received many research grants, including intensive industrial collaborations.