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|Title:||Efficient skyline maintenance for streaming data with partially-ordered domains||Authors:||Fang, Y.
|Issue Date:||2010||Citation:||Fang, 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. https://doi.org/10.1007/978-3-642-12026-8_26||Abstract:||We 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.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/40483||ISBN:||3642120253||ISSN:||03029743||DOI:||10.1007/978-3-642-12026-8_26|
|Appears in Collections:||Staff Publications|
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