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Grouping Time Series for Efficient Columnar Storage

Summary: Groups time series by sharing a single time column to reduce timestamp repetition, at the expense of potential nulls. NP-hard to optimize; propose heuristic grouping, deployed in Apache IoTDB, with storage comparable to single-column schemes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
heea0665cec34df5b
Venue
SIGMOD
Year
2023
Pagerank
4.9793485e-05
Overall Rank
11,696 | 21.37%
DOI
10.1145/3588703

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@inproceedings{fang_sigmod23,
        title = {{Grouping Time Series for Efficient Columnar Storage}},
        author = {Fang, Chenguang and Song, Shaoxu and Guan, Haoquan and Huang, Xiangdong and Wang, Chen and Wang, Jianmin},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588703},
        url = {https://dl.acm.org/doi/10.1145/3588703},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
3,622 Apache IoTDB: A Time Series Database for IoT Applications 2023 SIGMOD 7.1536644e-05
9,314 Apache TsFile: An IoT-native Time Series File Format 2024 VLDB 5.1959394e-05
11,315 Improving Time Series Data Compression in Apache IoTDB 2025 VLDB 4.9793485e-05
11,586 On Reducing Space Amplification with Multi-Column Compaction in Apache IoTDB 2024 VLDB 4.9793485e-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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