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Leveraging Process Mining on Service Events Towards Service Composition

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Advances in Services Computing (APSCC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9464))

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Abstract

Service composition is a widely-used approach in the development of applications. However, well-designed service composition approaches always lacks the consideration of execution environment, and the approach designed for application execution is usually incomplete and lacking necessary business consideration. In order to improve the comprehensiveness covered both design and execution stages, a service composition approach based on process mining is proposed. First, a meta-model is designed to connect the information of execution environment and business requirement. Next, the scene model based on this meta-model is generated by leveraging process mining. Then the scene model is applied to do service composition, including service selection from the Service Registry. After that, BPEL instance is converted based on aggregated scene information so as to enable application execution. Finally, a cloud-based logistics platform is implemented to verify the approach, and the result shows that the approach has high requirement accuracy and execution effectiveness.

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Acknowledgement

We would like to acknowledge the anonymous reviewers for their insightful and constructive comments and the support of the National Natural Science Foundation of China under No. 71171132 and No. 61373030.

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Correspondence to Yulai Li .

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© 2015 Springer International Publishing Switzerland

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Li, Y., Cai, H., Huang, C., Bu, F. (2015). Leveraging Process Mining on Service Events Towards Service Composition. In: Yao, L., Xie, X., Zhang, Q., Yang, L., Zomaya, A., Jin, H. (eds) Advances in Services Computing. APSCC 2015. Lecture Notes in Computer Science(), vol 9464. Springer, Cham. https://doi.org/10.1007/978-3-319-26979-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-26979-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26978-8

  • Online ISBN: 978-3-319-26979-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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