Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-12026-8_26
Title: Efficient skyline maintenance for streaming data with partially-ordered domains
Authors: Fang, Y.
Chan, C.-Y. 
Issue Date: 2010
Source: 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

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

3
checked on Dec 5, 2017

Page view(s)

52
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.