DocumentCode :
131005
Title :
Skyline query on uncertain data based on improved probabilistic constraint space algorithm
Author :
Dong Liming ; Liu Qing Bao ; Dai Changhua
Author_Institution :
Sci. & Technol. on Inf. Syst. Eng. Lab., Nat. Univ. of Defense Technol., Changsha, China
fYear :
2014
fDate :
27-29 June 2014
Firstpage :
929
Lastpage :
932
Abstract :
PCS algorithm is a non-indexing method to prune non-skyline objects. Its pruning rate is more than 90% when data dimension is no more than 4, however when data dimension exceeds 4 the pruning rate falls significantly. This article improved PCS algorithm to improve the pruning rate of high-dimension case. The main idea of PCS is to prune the non-skyline objects by establishing the minimum probabilistic constraint space. Experiments and analysis prove that IPCS improved an average 10% increase in the pruning rate.
Keywords :
Pareto analysis; data handling; probability; query processing; PCS algorithm; Pareto; data dimension; minimum probabilistic constraint space; nonindexing method; nonskyline object pruning; probabilistic constraint space algorithm; skyline query; uncertain data; Buildings; Educational institutions; Indexing; Laboratories; Probabilistic logic; Probability; IPCS; PCS; Skyline; uncertain data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on
Conference_Location :
Beijing
ISSN :
2327-0586
Print_ISBN :
978-1-4799-3278-8
Type :
conf
DOI :
10.1109/ICSESS.2014.6933717
Filename :
6933717
Link To Document :
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