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Robust and Fast Similarity Search for Moving Object Trajectories

Summary: EDR for moving-object trajectories; robust to noise, sensor errors, shifts, and scaling. Outperforms Euclidean, DTW, ERP; 50% more accurate than LCSS; three pruning techniques boost retrieval efficiency; experiments validate. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3722
Venue
SIGMOD
Year
2005
Pagerank
0.00024224879
Overall Rank
221 | 98.49%
DOI
10.1145/1066157.1066213

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{chen_sigmod05,
        title = {{Robust and Fast Similarity Search for Moving Object Trajectories}},
        author = {Chen, Lei and Özsu, M. Tamer and Oria, Vincent},
        series = {{SIGMOD} '05},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1066157.1066213},
        url = {https://dl.acm.org/doi/10.1145/1066157.1066213},
        year = {2005}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 53 citing papers.

Rank Citing Paper Year Venue Pagerank
12,188 Attraction and Avoidance Detection from Movements 2014 VLDB 5.093636e-05
12,190 MoveMine 2.0: Mining Object Relationships from Movement Data 2014 VLDB 5.093636e-05
12,662 UQLIPS: A Real-time Near-duplicate Video Clip Detection System 2007 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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