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The Scientific World Journal
Volume 2013 (2013), Article ID 729769, 10 pages
Research Article

Reliable Execution Based on CPN and Skyline Optimization for Web Service Composition

1College of Mathematics and Information Science, Network Engineering Technology Center, Weinan Normal University, Weinan 714000, China
2College of Communication Engineering, Network Engineering Technology Center, Weinan Normal University, Weinan 714000, China

Received 27 February 2013; Accepted 26 May 2013

Academic Editors: Y.-m. Cheung and Y. Wang

Copyright © 2013 Liping Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With development of SOA, the complex problem can be solved by combining available individual services and ordering them to best suit user’s requirements. Web services composition is widely used in business environment. With the features of inherent autonomy and heterogeneity for component web services, it is difficult to predict the behavior of the overall composite service. Therefore, transactional properties and nonfunctional quality of service (QoS) properties are crucial for selecting the web services to take part in the composition. Transactional properties ensure reliability of composite Web service, and QoS properties can identify the best candidate web services from a set of functionally equivalent services. In this paper we define a Colored Petri Net (CPN) model which involves transactional properties of web services in the composition process. To ensure reliable and correct execution, unfolding processes of the CPN are followed. The execution of transactional composition Web service (TCWS) is formalized by CPN properties. To identify the best services of QoS properties from candidate service sets formed in the TCSW-CPN, we use skyline computation to retrieve dominant Web service. It can overcome that the reduction of individual scores to an overall similarity leads to significant information loss. We evaluate our approach experimentally using both real and synthetically generated datasets.