In-Database Time Series Clustering
Summary: Proposes in-database K-Shape for time-series clustering across ranges, mitigating LSM-tree reordering and avoiding per-query full data loading. Introduces Medoid-Shape and its in-database variant for long series, with Apache IoTDB implementation and experiments showing higher efficiency with comparable accuracy. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yunxiang Su (Tsinghua University)
- 2. Kenny Ye Liang (Tsinghua University)
- 3. Shaoxu Song (Beijing Institute of Technology; Tsinghua University)
BibTeX Citation
@inproceedings{su_sigmod25,
title = {{In-Database Time Series Clustering}},
author = {Su, Yunxiang and Liang, Kenny Ye and Song, Shaoxu},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709696},
url = {https://dl.acm.org/doi/10.1145/3709696},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 41 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00046675394 |
| 962 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00012936472 |
| 1,579 | k-Shape: Efficient and Accurate Clustering of Time Series | 2015 | SIGMOD | 0.00010305183 |
| 1,805 | SAND: Streaming Subsequence Anomaly Detection | 2021 | VLDB | 9.7116108e-05 |
| 2,927 | In-Database Learning with Sparse Tensors | 2018 | PODS | 7.9531195e-05 |
| 3,152 | CDFShop: Exploring and Optimizing Learned Index Structures | 2020 | SIGMOD | 7.7003613e-05 |
| 5,038 | Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering | 2023 | VLDB | 6.3911449e-05 |
| 9,199 | On Repairing Timestamps for Regular Interval Time Series | 2022 | VLDB | 5.3058708e-05 |
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