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Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries

DC Field Value Language
dc.contributor.authorJang, Jun-Gi-
dc.contributor.authorKang, U-
dc.date.accessioned2022-06-24T00:25:46Z-
dc.date.available2022-06-24T00:25:46Z-
dc.date.created2022-05-09-
dc.date.issued2021-08-
dc.identifier.citationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.725-735-
dc.identifier.urihttps://hdl.handle.net/10371/183726-
dc.description.abstract© 2021 ACM.Given a temporal dense tensor and an arbitrary time range, how can we efficiently obtain latent factors in the range? Tucker decomposition is a fundamental tool for analyzing dense tensors to discover hidden factors, and has been exploited in many data mining applications. However, existing decomposition methods do not provide the functionality to analyze a specific range of a temporal tensor. The existing methods are one-off, with the main focus on performing Tucker decomposition once for a whole input tensor. Although a few existing methods with a preprocessing phase can deal with a time range query, they are still time-consuming and suffer from low accuracy. In this paper, we propose Zoom-Tucker, a fast and memory-efficient Tucker decomposition method for finding hidden factors of temporal tensor data in an arbitrary time range. Zoom-Tucker fully exploits block structure to compress a given tensor, supporting an efficient query and capturing local information. Zoom-Tucker answers diverse time range queries quickly and memory-efficiently, by elaborately decoupling the preprocessed results included in the range and carefully determining the order of computations. We demonstrate that Zoom-Tucker is up to 171.9x faster and requires up to 230x less space than existing methods while providing comparable accuracy.-
dc.language영어-
dc.publisherAssociation for Computing Machinery-
dc.titleFast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries-
dc.typeArticle-
dc.identifier.doi10.1145/3447548.3467290-
dc.citation.journaltitleProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-
dc.identifier.wosid000749556800071-
dc.identifier.scopusid2-s2.0-85114949137-
dc.citation.endpage735-
dc.citation.startpage725-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorKang, U-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
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