Parallel and progressive approaches for skyline query over probabilistic incomplete database

Zeng, Yifu, Li, Kenli, Yu, Shui, Zhou, Yantao and Li, Keqin 2018, Parallel and progressive approaches for skyline query over probabilistic incomplete database, IEEE access, vol. 6, pp. 13289-13301, doi: 10.1109/ACCESS.2018.2806379.

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Title Parallel and progressive approaches for skyline query over probabilistic incomplete database
Author(s) Zeng, Yifu
Li, Kenli
Yu, ShuiORCID iD for Yu, Shui
Zhou, Yantao
Li, Keqin
Journal name IEEE access
Volume number 6
Start page 13289
End page 13301
Total pages 13
Publisher IEEE Access
Place of publication Piscataway, N.J.
Publication date 2018-02-14
ISSN 2169-3536
Keyword(s) Science & Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Computer Science
Data management
incomplete data
parallel processing
progressive processing
probabilistic products
skyline query
Summary © 2018 IEEE. The advanced productivity of the modern society has created a wide range of similar commodities. However, the descriptions of commodities are always incomplete. Therefore, it is difficult for consumers to make choices. In the face of this problem, skyline query is a useful tool. However, the existing algorithms are unable to address incomplete probabilistic databases. In addition, it is necessary to wait for query completion to obtain even partial results. Furthermore, traditional skyline algorithms are usually serial. Thus, they cannot utilize multi-core processors effectively. Therefore, a parallel progressive skyline query algorithm for incomplete databases is imperative, which provides answers gradually and much faster. To address these problems, we design a new algorithm that uses multi-level grouping, pruning strategies, and pruning tuple transferring, which significantly decreases the computational costs. Experimental results demonstrate that the skyline results can be obtained in a short time. The parallel efficiency for an Octa-core processor reaches 90% on high-dimensional, large databases.
Language eng
DOI 10.1109/ACCESS.2018.2806379
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, IEEE
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Created: Mon, 18 Jun 2018, 11:47:27 EST

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