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SharkDB: An In-Memory Storage System for Massive Trajectory Data

Summary: SharkDB partitions trajectories into time-aligned frames stored as columnar in-memory blocks, enabling frame-level compression and cache-friendly processing. This frame-based in-memory column store enables parallel analytics on massive trajectories and outperforms disk/tuple-based designs for variable-length data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
he47921f108ca412c
Venue
SIGMOD
Year
2015
Pagerank
6.8213576e-05
Overall Rank
4,070 | 72.64%
DOI
10.1145/2723372.2735368

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod15,
        title = {{SharkDB: An In-Memory Storage System for Massive Trajectory Data}},
        author = {Wang, Haozhou and Zheng, Kai and Zhou, Xiaofang and Sadiq, Shazia},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2723372.2735368},
        url = {https://dl.acm.org/doi/10.1145/2723372.2735368},
        year = {2015}
}

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