Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-12026-8_26
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dc.titleEfficient skyline maintenance for streaming data with partially-ordered domains
dc.contributor.authorFang, Y.
dc.contributor.authorChan, C.-Y.
dc.date.accessioned2013-07-04T08:05:21Z
dc.date.available2013-07-04T08:05:21Z
dc.date.issued2010
dc.identifier.citationFang, Y.,Chan, C.-Y. (2010). Efficient skyline maintenance for streaming data with partially-ordered domains. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5981 LNCS (PART 1) : 322-336. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-12026-8_26" target="_blank">https://doi.org/10.1007/978-3-642-12026-8_26</a>
dc.identifier.isbn3642120253
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40483
dc.description.abstractWe address the problem of skyline query processing for a count-based window of continuous streaming data that involves both totally- and partially-ordered attribute domains. In this problem, a fixed-size buffer of the N most recent tuples is dynamically maintained and the key challenge is how to efficiently maintain the skyline of the sliding window of N tuples as new tuples arrive and old tuples expire. We identify the limitations of the state-of-the-art approach STARS, and propose two new approaches, STARS + and SkyGrid, to address its drawbacks. STARS + is an enhancement of STARS with three new optimization techniques, while SkyGrid is a simplification STARS that eliminates a key data structure used in STARS. While both new approaches outperform STARS significantly, the surprising result is that the best approach turns out to be the simplest approach, SkyGrid. © Springer-Verlag Berlin Heidelberg 2010.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-12026-8_26
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-12026-8_26
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5981 LNCS
dc.description.issuePART 1
dc.description.page322-336
dc.identifier.isiutNOT_IN_WOS
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