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Time Series Representation for Visualization in Apache IoTDB

Summary: Proposes M4-LSM, a chunk-merge-free M4 representation for time-series in LSM-based TSDBs, using chunk metadata and intra-chunk indexing to prune and avoid merges. Implemented in Apache IoTDB; real-data experiments show fast, precise M4 visualization with preserved accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6906
Venue
SIGMOD
Year
2024
Pagerank
5.4311904e-05
Overall Rank
8,409 | 42.31%
DOI
10.1145/3639290

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{rui_sigmod24,
        title = {{Time Series Representation for Visualization in Apache IoTDB}},
        author = {Rui, Lei and Huang, Xiangdong and Song, Shaoxu and Kang, Yuyuan and Wang, Chen and Wang, Jianmin},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3639290},
        url = {https://dl.acm.org/doi/10.1145/3639290},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
9,463 Hourglass: An Adaptive Range Filter with Lightweight Hybrid Encoding 2026 SIGMOD 5.2634238e-05
10,415 Visualization-Oriented Progressive Time Series Transformation 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

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