MOMENTI: Scalable Motif Mining in Multidimensional Time Series
Summary: MOMENTI: a subquadratic algorithm for top-k motif mining in multidimensional time series, returning exact motifs with probability ≥1−δ. Adaptive, memory-bounded, self-tuning method with probabilistic quality guarantees and orders-of-magnitude speedups versus prior quadratic exact algorithms. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,641 | Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science | 2018 | VLDB | 8.3905374e-05 |
| 5,245 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB | 5.6067361e-05 |
| 5,991 | Discovering Leitmotifs in Multidimensional Time Series | 2025 | VLDB | 5.2415551e-05 |
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