MOMENTI: Scalable Motif Mining in Multidimensional Time Series
Summary: MOMENTI enables scalable top-k motif mining over multidimensional time series in subquadratic time. Adaptive, distribution-aware tuning and memory limits yield the exact result with probability 1−δ, delivering orders-of-magnitude speedups. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Matteo Ceccarello (University of Padova)
- 2. Francesco Pio Monaco (University of Padova)
- 3. Francesco Silvestri (University of Padova)
BibTeX Citation
@article{ceccarello_vldb25,
title = {{MOMENTI: Scalable Motif Mining in Multidimensional Time Series}},
author = {Ceccarello, Matteo and Monaco, Francesco Pio and Silvestri, Francesco},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {10},
pages = {3463--3476},
doi = {10.14778/3748191.3748208},
url = {https://doi.org/10.14778/3748191.3748208},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,541 | Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science | 2018 | VLDB | 8.4500033e-05 |
| 5,266 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB | 6.2939621e-05 |
| 6,952 | Discovering Leitmotifs in Multidimensional Time Series | 2025 | VLDB | 5.7303405e-05 |
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