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Conformance Checking and QoS Selection Based on CPN for Web Service Composition.

Authors :
Ha, Weitao
Zhang, Guojun
Chen, Liping
Source :
International Journal of Pattern Recognition & Artificial Intelligence; Mar2015, Vol. 29 Issue 2, p-1, 16p
Publication Year :
2015

Abstract

The development of new services by composition of existing ones has gained considerable momentum as a means of integrating heterogeneous applications and realizing business collaborations. More and more Web services with similar function attributes but different QoS are available. The performance of the composed service is determined by the involved Web services. Therefore, QoS properties are crucial for selecting the Web services to take part in the composition, which can identify the best candidate Web services from a set of functionally-equivalent services. Web service composition enables seamless and dynamic integration of business applications on the web. Due to the inherent autonomy and heterogeneity of component Web services, it is difficult to predict the behavior of the overall composite service. Conformance checking identifies failure and conflict of execution of composite Web service that ensures reliable execution. In this paper we use skyline computation to select services for composition efficiently, reducing the number of candidate services to be considered. Then a novel color Petri net model of Web service composition is presented that combines QoS-based optimal service selection and consistence verification. In the model we define aggregation functions, and use a Multiple Attribute Decision Making approach for the utility function to achieve optimal services selection of QoS properties. We also propose a consistence verification approach to identify potential logical inconsistence of the semantic Web service process before the deployment. Proofs are also presented. We evaluate our approach experimentally using both real and synthetically generated datasets. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02180014
Volume :
29
Issue :
2
Database :
Complementary Index
Journal :
International Journal of Pattern Recognition & Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
108351170
Full Text :
https://doi.org/10.1142/S0218001415590028