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dc.contributor.authorWang, Hao
dc.contributor.authorYang, Chengcheng
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorGao, Xin
dc.date.accessioned2020-09-27T04:47:26Z
dc.date.available2020-09-27T04:47:26Z
dc.date.issued2020-09-17
dc.date.submitted2020-07-20
dc.identifier.citationWang, H., Yang, C., Zhang, X., & Gao, X. (2020). Efficient locality-sensitive hashing over high-dimensional streaming data. Neural Computing and Applications. doi:10.1007/s00521-020-05336-1
dc.identifier.issn1433-3058
dc.identifier.issn0941-0643
dc.identifier.doi10.1007/s00521-020-05336-1
dc.identifier.urihttp://hdl.handle.net/10754/665289
dc.description.abstractApproximate nearest neighbor (ANN) search in high-dimensional spaces is fundamental in many applications. Locality-sensitive hashing (LSH) is a well-known methodology to solve the ANN problem. Existing LSH-based ANN solutions typically employ a large number of individual indexes optimized for searching efficiency. Updating such indexes might be impractical when processing high-dimensional streaming data. In this paper, we present a novel disk-based LSH index that offers efficient support for both searches and updates. The contributions of our work are threefold. First, we use the write-friendly LSM-trees to store the LSH projections to facilitate efficient updates. Second, we develop a novel estimation scheme to estimate the number of required LSH functions, with which the disk storage and access costs are effectively reduced. Third, we exploit both the collision number and the projection distance to improve the efficiency of candidate selection, improving the search performance with theoretical guarantees on the result quality. Experiments on four real-world datasets show that our proposal outperforms the state-of-the-art schemes.
dc.description.sponsorshipThe authors would like to thank the editor and anonymous reviewers for their valuable suggestions and comments. This work was funded in part by the Center of Excellence for NEOM Research at KAUST, REI/1/4178-01-01, the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under award numbers BAS/1/1624-01, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, and REI/1/4473-01-01.
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttp://link.springer.com/10.1007/s00521-020-05336-1
dc.rightsArchived with thanks to Neural Computing and Applications
dc.titleEfficient locality-sensitive hashing over high-dimensional streaming data
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.contributor.departmentMachine Intelligence and kNowledge Engineering Laboratory, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalNeural Computing and Applications
dc.rights.embargodate2021-09-17
dc.eprint.versionPost-print
dc.contributor.institutionShenzhen University, Shenzhen, China
kaust.personWang, Hao
kaust.personYang, Chengcheng
kaust.personZhang, Xiangliang
kaust.personGao, Xin
kaust.grant.numberBAS/1/1624
dc.date.accepted2020-09-02
dc.identifier.eid2-s2.0-85091161860
refterms.dateFOA2020-12-09T13:36:53Z
kaust.acknowledged.supportUnitCenter of Excellence for NEOM Research
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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