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
- 2. Kenny Ye Liang
- 3. Shaoxu Song
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Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 65 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00062029383 |
| 961 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00015001792 |
| 1,516 | k-Shape: Efficient and Accurate Clustering of Time Series | 2015 | SIGMOD | 0.00011586255 |
| 2,029 | SAND: Streaming Subsequence Anomaly Detection | 2021 | VLDB | 9.740868e-05 |
| 4,060 | CDFShop: Exploring and Optimizing Learned Index Structures | 2020 | SIGMOD | 6.4836825e-05 |
| 6,851 | Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering | 2023 | VLDB | 4.9084229e-05 |
| 9,048 | On Repairing Timestamps for Regular Interval Time Series | 2022 | VLDB | 4.4039656e-05 |
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| Overall Rank | Paper | Year | Venue | Pagerank |
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| 9,048 | On Repairing Timestamps for Regular Interval Time Series | 2022 | VLDB | 4.4039656e-05 |
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| 5,071 | Time Series Data Encoding for Efficient Storage: A Comparative Analysis in Apache IoTDB | 2022 | VLDB | 5.7188461e-05 |
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