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Computing Service Skyline from Uncertain QoWS

Yu, Qi; Bouguettaya, Athman

Description

The performance of a service provider may fluctuate due to the dynamic service environment. Thus, the quality of service actually delivered by a service provider is inherently uncertain. Existing service optimization approaches usually assume that the quality of service does not change over time. Moreover, most of these approaches rely on computing a predefined objective function. When multiple quality criteria are considered, users are required to express their preference over different (and...[Show more]

dc.contributor.authorYu, Qi
dc.contributor.authorBouguettaya, Athman
dc.date.accessioned2015-12-10T22:32:45Z
dc.identifier.issn1939-1374
dc.identifier.urihttp://hdl.handle.net/1885/55906
dc.description.abstractThe performance of a service provider may fluctuate due to the dynamic service environment. Thus, the quality of service actually delivered by a service provider is inherently uncertain. Existing service optimization approaches usually assume that the quality of service does not change over time. Moreover, most of these approaches rely on computing a predefined objective function. When multiple quality criteria are considered, users are required to express their preference over different (and sometimes conflicting) quality attributes as numeric weights. This is rather a demanding task and an imprecise specification of the weights could miss user-desired services. We present a novel concept, called p-dominant service skyline. A provider S belongs to the p-dominant skyline if the chance that S is dominated by any other provider is less than p. Computing the p-dominant skyline provides an integrated solution to tackle the above two issues simultaneously. We present a p-R-tree indexing structure and a dual-pruning scheme to efficiently compute the p-dominant skyline. We assess the efficiency of the proposed algorithm with an analytical study and extensive experiments.
dc.publisherIEEE Computer Society
dc.sourceIEEE Transactions on Services Computing
dc.subjectKeywords: Computing services; Dynamic services; Indexing structures; Integrated solutions; Multiple quality; Novel concept; Objective functions; Quality attributes; Service optimization; Service provider; Service selection; Algorithms; Decision trees; Optimization; Quality of service; Service optimization; Service selection; Skyline analysis; Uncertainty
dc.titleComputing Service Skyline from Uncertain QoWS
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume3
dc.date.issued2010
local.identifier.absfor080704 - Information Retrieval and Web Search
local.identifier.ariespublicationU3594520xPUB344
local.type.statusPublished Version
local.contributor.affiliationYu, Qi, Rochester Institute of Technology
local.contributor.affiliationBouguettaya, Athman, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.issue1
local.bibliographicCitation.startpage16
local.bibliographicCitation.lastpage29
local.identifier.doi10.1109/TSC.2010.7
local.identifier.absseo890301 - Electronic Information Storage and Retrieval Services
dc.date.updated2016-02-24T10:18:08Z
local.identifier.scopusID2-s2.0-77950636557
CollectionsANU Research Publications

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