Predictive Indexing for Position Data of Moving Objects in the Real World

  • Yutaka Yanagisawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5072)


We propose a spatial-temporal indexing method for moving objects based on a prediction technique using motion patterns extracted from practical data, such as trajectories of pedestrians. To build an efficient index structure, we conducted an experiment to analyze practical moving objects, such as people walking in a hall. As a result, we found that any moving objects can be classified into just three types of motion characteristics: 1) staying, 2) straight-moving, 3) random walking. Indexing systems can predict highly accurate future positions of each object based on our found characteristics; moreover, the indexing system can build efficient MBRs in the spatial-temporal data structure. To show the advantage of our prediction method over previous works, we conducted an experiment to evaluate the performance of each prediction method.


Motion Pattern Position Data Indexing Mechanism Prediction Technique Trajectory Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yutaka Yanagisawa
    • 1
  1. 1.NTT Communication Science Laboratories 

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