VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series
Summary: VALMOD enables scalable, exact discovery of variable-length motifs in data series, avoiding brute-force across lengths. It yields a length-invariant motif ranking and a meta-data structure to guide length choice, with a visualization-driven demo. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Michele Linardi (Paris Descartes University)
- 2. Yan Zhu (University of California Riverside)
- 3. Themis Palpanas (Paris Descartes University)
- 4. Eamonn Keogh (University of California Riverside)
BibTeX Citation
@inproceedings{linardi_sigmod18,
title = {{VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series}},
author = {Linardi, Michele and Zhu, Yan and Palpanas, Themis and Keogh, Eamonn},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3193556},
url = {https://dl.acm.org/doi/10.1145/3183713.3193556},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,486 | Scalable, Variable-Length Similarity Search in Data Series: The ULISSE Approach | 2018 | VLDB | 7.3696676e-05 |
| 5,266 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB | 6.2939621e-05 |
| 6,784 | Motiflets - Simple and Accurate Detection of Motifs in Time Series | 2023 | VLDB | 5.7758194e-05 |
| 6,952 | Discovering Leitmotifs in Multidimensional Time Series | 2025 | VLDB | 5.7303405e-05 |
| 10,828 | TELESAFE: Detecting Private/Work Boundary Crossings in Energy Consumption Trails in Telework | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,506 | Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series | 2018 | SIGMOD | 6.6569729e-05 |
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