Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping

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dc.contributor.authorYoon, Susikko
dc.contributor.authorLee, Jae-Gilko
dc.contributor.authorLee, Byung Sukko
dc.date.accessioned2020-11-05T04:55:25Z-
dc.date.available2020-11-05T04:55:25Z-
dc.date.created2020-11-05-
dc.date.issued2020-08-25-
dc.identifier.citation26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020, pp.1181 - 1191-
dc.identifier.urihttp://hdl.handle.net/10203/277127-
dc.description.abstractReal-time outlier detection from a data stream is an increasingly important problem, especially as sensor-generated data streams abound in many applications owing to the prevalence of IoT and emergence of digital twins. Several density-based approaches have been proposed to address this problem, but arguably none of them is fast enough to meet the performance demand of real applications. This paper is founded upon a novel observation that, in many regions of the data space, data distributions hardly change across window slides. We propose a new algorithm, abbr. STARE, which identifies local regions in which data distributions hardly change and then skips updating the densities in those regions-a notion called stationary region skipping. Two techniques, data distribution approximation and cumulative net-change-based skip, are employed to efficiently and effectively implement the notion. Extensive experiments using synthetic and real data streams as well as a case study show that STARE is several orders of magnitude faster than the existing algorithms while achieving comparable or higher accuracy.-
dc.languageEnglish-
dc.publisherAssociation for Computing Machinery-
dc.titleUltrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping-
dc.typeConference-
dc.identifier.scopusid2-s2.0-85090420706-
dc.type.rimsCONF-
dc.citation.beginningpage1181-
dc.citation.endingpage1191-
dc.citation.publicationname26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationVirtual-
dc.identifier.doi10.1145/3394486.3403171-
dc.contributor.localauthorLee, Jae-Gil-
dc.contributor.nonIdAuthorLee, Byung Suk-
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CS-Conference Papers(학술회의논문)
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