DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Susik | ko |
dc.contributor.author | Lee, Jae-Gil | ko |
dc.contributor.author | Lee, Byung Suk | ko |
dc.date.accessioned | 2020-11-05T04:55:25Z | - |
dc.date.available | 2020-11-05T04:55:25Z | - |
dc.date.created | 2020-11-05 | - |
dc.date.issued | 2020-08-25 | - |
dc.identifier.citation | 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020, pp.1181 - 1191 | - |
dc.identifier.uri | http://hdl.handle.net/10203/277127 | - |
dc.description.abstract | Real-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.language | English | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85090420706 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 1181 | - |
dc.citation.endingpage | 1191 | - |
dc.citation.publicationname | 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1145/3394486.3403171 | - |
dc.contributor.localauthor | Lee, Jae-Gil | - |
dc.contributor.nonIdAuthor | Lee, Byung Suk | - |
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