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As a case study of a data-driven science workflow, we illustrate how domain knowledge can be incorporated into the workload to improve both the efficiency and result quality. We describe several end-to-end optimizations of the analysis pipeline from pre-processing to post-processing, which allow the application to scale to time series data measured at multiple seismic stations. Our optimizations enable an over 100\u00d7 speedup in the end-to-end analysis pipeline. This improved scalability enabled seismologists to perform seismic analysis on more than ten years of continuous time series data from over ten seismic stations, and has directly enabled the discovery of 597 new earthquakes near the Diablo Canyon nuclear power plant in California and 6123 new earthquakes in New Zealand.<\/jats:p>","DOI":"10.14778\/3236187.3236214","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1674-1687","source":"Crossref","is-referenced-by-count":32,"title":["Locality-sensitive hashing for earthquake detection"],"prefix":"10.14778","volume":"11","author":[{"given":"Kexin","family":"Rong","sequence":"first","affiliation":[{"name":"Stanford University"}]},{"given":"Clara E.","family":"Yoon","sequence":"additional","affiliation":[{"name":"Stanford University"}]},{"given":"Karianne J.","family":"Bergen","sequence":"additional","affiliation":[{"name":"Stanford University"}]},{"given":"Hashem","family":"Elezabi","sequence":"additional","affiliation":[{"name":"Stanford University"}]},{"given":"Peter","family":"Bailis","sequence":"additional","affiliation":[{"name":"Stanford University"}]},{"given":"Philip","family":"Levis","sequence":"additional","affiliation":[{"name":"Stanford University"}]},{"given":"Gregory C.","family":"Beroza","sequence":"additional","affiliation":[{"name":"Stanford University"}]}],"member":"320","published-online":{"date-parts":[[2018,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"FALCONN - FAst Lookups of Cosine and Other Nearest Neighbors. https:\/\/github.com\/falconn-lib\/falconn. 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