Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Patten Mining System

Phan Nhat Hai 1 Dino Ienco 1 Pascal Poncelet 1 Maguelonne Teisseire 1
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Recent improvements in positioning technology has led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. Usually, these object sets are called spatio-temporal patterns. Analyzing such data has been applied in many real world applications, e.g., in ecological study, vehi- cle control, mobile communication management, etc. However, few data mining tools are available for flexible and scalable analysis of massive scale moving object data. The main reason is that there is no a uni- fying approach to manage the patterns while many different kinds of spatio-temporal patterns have been proposed in recent years. Each ap- proach only focuses on mining a specific kind of pattern. Our system, GeT Move, is designed to extract and manage different spatio-temporal patterns concurrently. A user-friendly interface is provided to facilitate interactive exploration of mining results. Since GeT Move is tested on many kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data by exhibiting different kinds of patterns at the same time.
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Phan Nhat Hai, Dino Ienco, Pascal Poncelet, Maguelonne Teisseire. Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Patten Mining System. ECML PKDD: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2012, Bristol, United Kingdom. pp.820-823. ⟨lirmm-00732662⟩

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