Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/109946
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Type: Journal article
Title: Keyword search for building service-based systems
Author: He, Q.
Zhou, R.
Zhang, X.
Wang, Y.
Ye, D.
Chen, F.
Grundy, J.
Yang, Y.
Citation: IEEE Transactions on Software Engineering, 2017; 43(7):658-674
Publisher: IEEE
Issue Date: 2017
ISSN: 0098-5589
1939-3520
Statement of
Responsibility: 
Qiang He, Rui Zhou, Xuyun Zhang, Yanchun Wang, Dayong Ye, Feifei Chen, John C. Grundy and Yun Yang
Abstract: With the fast growth of applications of service-oriented architecture (SOA) in software engineering, there has been a rapid increase in demand for building service-based systems (SBSs) by composing existing Web services. Finding appropriate component services to compose is a key step in the SBS engineering process. Existing approaches require that system engineers have detailed knowledge of SOA techniques which is often too demanding. To address this issue, we propose Keyword Search for Service-based Systems (KS3), a novel approach that integrates and automates the system planning, service discovery and service selection operations for building SBSs based on keyword search. KS3 assists system engineers without detailed knowledge of SOA techniques in searching for component services to build SBSs by typing a few keywords that represent the tasks of the SBSs with quality constraints and optimisation goals for system quality, e.g., reliability, throughput and cost. KS3 offers a new paradigm for SBS engineering that can significantly save the time and effort during the system engineering process. We conducted large-scale experiments using two real-world Web service datasets to demonstrate the practicality, effectiveness and efficiency of KS3.
Keywords: Service-based system; keyword search; service composition; web service; quality of service; cloud computing
Rights: © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information
RMID: 0030076409
DOI: 10.1109/TSE.2016.2624293
Grant ID: http://purl.org/au-research/grants/arc/DP150101775
Appears in Collections:Computer Science publications

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