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Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science

Summary: LSH-based similarity search on seismic time series for earthquake detection; case study on scaling from a single station to multi-station, multi-year workloads. End-to-end optimizations yield >100x speedup, enabling discovery of 597 earthquakes near Diablo Canyon and 6,123 in New Zealand. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11839
Venue
VLDB
Year
2018
Pagerank
8.4500033e-05
Overall Rank
2,541 | 82.57%
DOI
10.14778/3236187.3236214

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{rong_vldb18,
        title = {{Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science}},
        author = {Rong, Kexin and Yoon, Clara E. and Bergen, Karianne J. and Elezabi, Hashem and Bailis, Peter and Levis, Philip and Beroza, Gregory C.},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {11},
        pages = {1674--1687},
        doi = {10.14778/3236187.3236214},
        url = {https://doi.org/10.14778/3236187.3236214},
        year = {2018}
}

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