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Mining Frequent Trajectories of Moving Objects for Location Prediction

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4571))

Abstract

Advances in wireless and mobile technology flood us with amounts of moving object data that preclude all means of manual data processing. The volume of data gathered from position sensors of mobile phones, PDAs, or vehicles, defies human ability to analyze the stream of input data. On the other hand, vast amounts of gathered data hide interesting and valuable knowledge patterns describing the behavior of moving objects. Thus, new algorithms for mining moving object data are required to unearth this knowledge. An important function of the mobile objects management system is the prediction of the unknown location of an object. In this paper we introduce a data mining approach to the problem of predicting the location of a moving object. We mine the database of moving object locations to discover frequent trajectories and movement rules. Then, we match the trajectory of a moving object with the database of movement rules to build a probabilistic model of object location. Experimental evaluation of the proposal reveals prediction accuracy close to 80%. Our original contribution includes the elaboration on the location prediction model, the design of an efficient mining algorithm, introduction of movement rule matching strategies, and a thorough experimental evaluation of the proposed model.

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Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland

    Mikołaj Morzy

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  1. Mikołaj Morzy
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Petra Perner

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© 2007 Springer-Verlag Berlin Heidelberg

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Morzy, M. (2007). Mining Frequent Trajectories of Moving Objects for Location Prediction. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_50

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  • DOI: https://doi.org/10.1007/978-3-540-73499-4_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73498-7

  • Online ISBN: 978-3-540-73499-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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