Rare Time Series Motif Discovery from Unbounded Streams
Summary: Rare time-series motifs in unbounded streams; exact definitions infeasible under realistic assumptions. Efficient probabilistic framework achieves high probability of motif discovery, guided by cross-domain usage patterns in EEG, entomology, nematology. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Nurjahan Begum
- 2. Eamonn Keogh
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,853 | Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures | 2020 | SIGMOD | 5.8760276e-05 |
| 5,324 | Clustering Stream Data by Exploring the Evolution of Density Mountain | 2018 | VLDB | 5.5691645e-05 |
| 10,466 | A Structured Study of Multivariate Time-Series Distance Measures | 2025 | SIGMOD | 4.1945683e-05 |
| 10,739 | Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods | 2025 | VLDB | 4.1945683e-05 |
| 11,235 | Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances | 2023 | VLDB | 4.1945683e-05 |
| 11,727 | Vocalizing Large Time Series Efficiently | 2018 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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