Set-based Similarity Search for Time Series
Summary: STS3 converts time series into sets and uses Jaccard similarity for k-NN search. With indexing, pruning, and approximation techniques, it achieves faster queries and competitive accuracy versus DTW on large datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jinglin Peng (Harbin Engineering University)
- 2. Hongzhi Wang (Harbin Engineering University)
- 3. Jianzhong Li (Harbin Engineering University)
- 4. Hong Gao (Harbin Engineering University)
BibTeX Citation
@inproceedings{peng_sigmod16,
title = {{Set-based Similarity Search for Time Series}},
author = {Peng, Jinglin and Wang, Hongzhi and Li, Jianzhong and Gao, Hong},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2882963},
url = {https://dl.acm.org/doi/10.1145/2882903.2882963},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,568 | Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science | 2018 | VLDB | 8.2870435e-05 |
| 5,444 | Pigeonring: A Principle for Faster Thresholded Similarity Search | 2019 | VLDB | 6.1268526e-05 |
| 11,885 | Fast Dataset Search with Earth Mover's Distance | 2022 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 42 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00045773967 |
| 193 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD | 0.00025648171 |
| 227 | Robust and Fast Similarity Search for Moving Object Trajectories | 2005 | SIGMOD | 0.0002393284 |
| 306 | On The Marriage of Lp-norms and Edit Distance | 2004 | VLDB | 0.00021575844 |
| 2,494 | An Efficient and Accurate Method for Evaluating Time Series Similarity | 2007 | SIGMOD | 8.3888883e-05 |
| 3,333 | FTW: Fast Similarity Search under the Time Warping Distance | 2005 | PODS | 7.4153527e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,299 | Efficient and Effective KNN Sequence Search with Approximate n-grams | 2014 | VLDB |
| 2 | 6,627 | Interactive Time Series Exploration Powered by the Marriage of Similarity Distances | 2017 | VLDB |
| 3 | 3,302 | Fast Time-Series Searching with Scaling and Shifting | 1999 | PODS |
| 4 | 385 | Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases | 1995 | VLDB |
| 5 | 4,304 | Data Series Progressive Similarity Search with Probabilistic Quality Guarantees | 2020 | SIGMOD |
| 6 | 3,333 | FTW: Fast Similarity Search under the Time Warping Distance | 2005 | PODS |
| 7 | 6,890 | On Efficiently Searching Trajectories and Archival Data for Historical Similarities | 2008 | VLDB |
| 8 | 1,095 | A Data-adaptive and Dynamic Segmentation Index for Whole Matching on Time Series | 2013 | VLDB |
| 9 | 8,286 | Anticipatory DTW for Efficient Similarity Search in Time Series Databases | 2009 | VLDB |
| 10 | 829 | Similarity-Based Queries for Time Series Data | 1997 | SIGMOD |