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Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries
DC Field | Value | Language |
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dc.contributor.author | Jang, Jun-Gi | - |
dc.contributor.author | Kang, U | - |
dc.date.accessioned | 2022-06-24T00:25:46Z | - |
dc.date.available | 2022-06-24T00:25:46Z | - |
dc.date.created | 2022-05-09 | - |
dc.date.issued | 2021-08 | - |
dc.identifier.citation | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.725-735 | - |
dc.identifier.uri | https://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.language | 영어 | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries | - |
dc.type | Article | - |
dc.identifier.doi | 10.1145/3447548.3467290 | - |
dc.citation.journaltitle | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | - |
dc.identifier.wosid | 000749556800071 | - |
dc.identifier.scopusid | 2-s2.0-85114949137 | - |
dc.citation.endpage | 735 | - |
dc.citation.startpage | 725 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Kang, U | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
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