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Ghost: A General Framework for High-Performance Online Similarity Queries over Distributed Trajectory Streams

Summary: Ghost is a distributed framework for online trajectory similarity search and join over real-time streams, with IOSC making pairwise distance linear in trajectory length. Histogram indexes with pruning bounds support streaming similarity on Flink via CostPartitioner, achieving 6-20x throughput and memory savings. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6738
Venue
SIGMOD
Year
2023
Pagerank
5.3766157e-05
Overall Rank
8,734 | 40.08%
DOI
10.1145/3589318

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{fang_sigmod23,
        title = {{Ghost: A General Framework for High-Performance Online Similarity Queries over Distributed Trajectory Streams}},
        author = {Fang, Ziquan and Gong, Shenghao and Chen, Lu and Xu, Jiachen and Gao, Yunjun and Jensen, Christian S.},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589318},
        url = {https://dl.acm.org/doi/10.1145/3589318},
        year = {2023}
}

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