Robust and fast similarity search for moving object trajectories

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Robust and fast similarity search for moving object trajectories (2005)

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by Lei Chen , M. Tamer Özsu , Vincent Oria

Venue:	In Proc. ACM SIGMOD Int. Conf. on Management of Data
Citations:	155 - 14 self

BibTeX

@INPROCEEDINGS{Chen05robustand,
    author = {Lei Chen and M. Tamer Özsu and Vincent Oria},
    title = {Robust and fast similarity search for moving object trajectories},
    booktitle = {In Proc. ACM SIGMOD Int. Conf. on Management of Data},
    year = {2005}
}

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Abstract

An important consideration in similarity-based retrieval of moving object trajectories is the definition of a distance function. The ex-isting distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, disturbance signals, and different sampling rates. Cleaning data to eliminate these is not always possible. In this paper, we introduce a novel distance function, Edit Distance on Real sequence (EDR) which is robust against these data imper-fections. Analysis and comparison of EDR with other popular dis-tance functions, such as Euclidean distance, Dynamic Time Warp-ing (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences (LCSS), indicate that EDR is more robust than Euclidean distance, DTW and ERP, and it is on average 50% more accurate than LCSS. We also develop three pruning tech-niques to improve the retrieval efficiency of EDR and show that these techniques can be combined effectively in a search, increas-ing the pruning power significantly. The experimental results con-firm the superior efficiency of the combined methods. 1.

Keyphrases

object trajectory fast similarity search euclidean distance edit distance retrieval efficiency real sequence different sampling rate cleaning data detection technique important consideration dynamic time warp-ing popular dis-tance function data imper-fections novel distance function similarity-based retrieval ex-isting distance function disturbance signal distance function real penalty superior efficiency combined method experimental result longest common subsequence

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