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Conference Paper: OLAP on sequence data

TitleOLAP on sequence data
Authors
Lo, EKao, BHo, WSLee, SDChui, CKCheung, DW
KeywordsAlgorithms
Design
Performance
Issue Date2008
PublisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://www.acm.org/sigmod
Citation
The 2008 ACM SIGMOD International Conference on Management of Data (SIGMOD’08), Vancouver, BC, Canada, 9-12 June 2008. In Proceedings of SIGMOD’08, 2008, p. 649-660 How to Cite?
DOI: http://dx.doi.org/10.1145/1376616.1376682
AbstractMany kinds of real-life data exhibit logical ordering among their data items and are thus sequential in nature. However, traditional online analytical processing (OLAP) systems and techniques were not designed for sequence data and they are incapable of supporting sequence data analysis. In this paper, we propose the concept of Sequence OLAP, or S-OLAP for short. The biggest distinction of S-OLAP from traditional OLAP is that a sequence can be characterized not only by the attributes' values of its constituting items, but also by the subsequence/substring patterns it possesses. This paper studies many aspects related to Sequence OLAP. The concepts of sequence cuboid and sequence data cube are introduced. A prototype S-OLAP system is built in order to validate the proposed concepts. The prototype is able to support "pattern-based" grouping and aggregation, which is currently not supported by any OLAP system. The implementation details of the prototype system as well as experimental results are presented. Copyright 2008 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/93352
ISBN
978-1-60558-102-6
ISSN
0730-8078
2023 SCImago Journal Rankings: 2.640
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorLo, Een_HK
dc.contributor.authorKao, Ben_HK
dc.contributor.authorHo, WSen_HK
dc.contributor.authorLee, SDen_HK
dc.contributor.authorChui, CKen_HK
dc.contributor.authorCheung, DWen_HK
dc.date.accessioned2010-09-25T14:58:29Z-
dc.date.available2010-09-25T14:58:29Z-
dc.date.issued2008en_HK
dc.identifier.citationThe 2008 ACM SIGMOD International Conference on Management of Data (SIGMOD’08), Vancouver, BC, Canada, 9-12 June 2008. In Proceedings of SIGMOD’08, 2008, p. 649-660en_HK
dc.identifier.isbn978-1-60558-102-6-
dc.identifier.issn0730-8078en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93352-
dc.description.abstractMany kinds of real-life data exhibit logical ordering among their data items and are thus sequential in nature. However, traditional online analytical processing (OLAP) systems and techniques were not designed for sequence data and they are incapable of supporting sequence data analysis. In this paper, we propose the concept of Sequence OLAP, or S-OLAP for short. The biggest distinction of S-OLAP from traditional OLAP is that a sequence can be characterized not only by the attributes' values of its constituting items, but also by the subsequence/substring patterns it possesses. This paper studies many aspects related to Sequence OLAP. The concepts of sequence cuboid and sequence data cube are introduced. A prototype S-OLAP system is built in order to validate the proposed concepts. The prototype is able to support "pattern-based" grouping and aggregation, which is currently not supported by any OLAP system. The implementation details of the prototype system as well as experimental results are presented. Copyright 2008 ACM.en_HK
dc.languageengen_HK
dc.publisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://www.acm.org/sigmoden_HK
dc.relation.ispartofProceedings of the ACM SIGMOD International Conference on Management of Dataen_HK
dc.rightsProceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD’08. Copyright © Association for Computing Machinery.-
dc.subjectAlgorithmsen_HK
dc.subjectDesignen_HK
dc.subjectPerformanceen_HK
dc.titleOLAP on sequence dataen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailKao, B: kao@cs.hku.hken_HK
dc.identifier.emailHo, WS: wsho@cs.hku.hken_HK
dc.identifier.emailCheung, DW: dcheung@cs.hku.hken_HK
dc.identifier.authorityKao, B=rp00123en_HK
dc.identifier.authorityHo, WS=rp01730en_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1145/1376616.1376682en_HK
dc.identifier.scopuseid_2-s2.0-57149146704en_HK
dc.identifier.hkuros141137en_HK
dc.identifier.hkuros144336-
dc.identifier.hkuros200228-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-57149146704&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage649en_HK
dc.identifier.epage660en_HK
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe 2008 ACM SIGMOD International Conference on Management of Data (SIGMOD’08), Vancouver, BC, Canada, 9-12 June 2008. In Proceedings of SIGMOD’08, 2008, p. 649-660-
dc.identifier.scopusauthoridLo, E=14028731900en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.scopusauthoridHo, WS=7402968940en_HK
dc.identifier.scopusauthoridLee, SD=52963875200en_HK
dc.identifier.scopusauthoridChui, CK=21741958100en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.citeulike4135471-
dc.identifier.issnl0730-8078-

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