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Searching Trajectories by Locations - An Efficiency Study

Summary: Defines k-BCT: top-k trajectories that best connect a small query-location set using distance+order similarity. Uses Incremental kNN (IKNN) on a spatial index with lower/upper bound pruning; adapts best-first/DFS k-NN and demonstrates optimizations and experiments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4311
Venue
SIGMOD
Year
2010
Pagerank
6.7647222e-05
Overall Rank
4,316 | 70.39%
DOI
10.1145/1807167.1807197

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{chen_sigmod10,
        title = {{Searching Trajectories by Locations - An Efficiency Study}},
        author = {Chen, Zaiben and Shen, Heng Tao and Zhou, Xiaofang and Zheng, Yu and Xie, Xing},
        series = {{SIGMOD} '10},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1807167.1807197},
        url = {https://dl.acm.org/doi/10.1145/1807167.1807197},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
3,852 On the Spatiotemporal Burstiness of Terms 2012 VLDB 7.0734721e-05
4,475 UlTraMan: A Unified Platform for Big Trajectory Data Management and Analytics 2018 VLDB 6.6803264e-05
6,285 Trajectory Similarity Join in Spatial Networks 2017 VLDB 5.9270112e-05
6,390 Trip Planning by an Integrated Search Paradigm 2018 SIGMOD 5.8895166e-05
11,850 Top-k Queries over Digital Traces 2019 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

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

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