Discovery and Analysis About the Evolution of Service Composition Patterns

Authors

  • Zhenfeng Gao Graduate School at Shenzhen, Tsinghua University, Shenzhen, China and Sangfor Technologies Inc., Shenzhen, China
  • Yushun Fan Tsinghua National Laboratory for Information Science and Technology, Beijing, China
  • Xiu Li Graduate School at Shenzhen, Tsinghua University, Shenzhen, China
  • Liang Gu Sangfor Technologies Inc., Shenzhen, China
  • Cheng Wu Tsinghua National Laboratory for Information Science and Technology, Beijing, China
  • Jia Zhang Department of Electrical and Computer Engineering, Carnegie Mellon University, Silicon Valley, California, USA

DOI:

https://doi.org/10.13052/jwe1540-9589.1872

Keywords:

Topic evolution graph, service composition recommendation, topic model

Abstract

Service ecosystems, consisting of various kinds of services and mashups, usually keep evolving over time. Existing works on the evolution of service ecosystems focus on either evaluating the impacts of single services’ changes on the usage of services and the stability of the whole ecosystem, or discovering co-occurrence relationship between services, but fail to disclose any knowledge from the aspect of the evolution of service composition patterns. Based on our previous work, this paper moves one step further, revealing the latent service composition trends in a service ecosystem and providing more distinct explanation of different topic evolution patterns. A novel methodology, named Extended Dependency-Compensated Service Co-occurrence LDA (EDC-SeCo-LDA), is developed to calculate the directed dependencies between different topics and build topic evolution graph. The evolution trend of service composition could be disclosed by the graph intuitively. What’s more, EDC-SeCo-LDA proposes five different ways to adopt dependency compensation to improve the performance when making service recommendation. Experiments on ProgrammableWeb.com show that EDC-SeCo-LDA can reveal significant topic dependencies, and recommend service composition more effectively, i.e., 6% better in terms of Mean Average Precision compared with baseline approaches.

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Author Biographies

Zhenfeng Gao, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China and Sangfor Technologies Inc., Shenzhen, China

Zhenfeng Gao received the PhD degree in control theory and application in 2018 from Tsinghua University, China. He is currently working as the postdoctor at the Graduate school at Shenzhen, Tsinghua University as well as the postdoctoral research center at Sangfor Technologies Inc. His research interests include services computing, service recommendation, big data and blockchain technology.

Yushun Fan, Tsinghua National Laboratory for Information Science and Technology, Beijing, China

Yushun Fan received the PhD degree in control theory and application from Tsinghua University, China, in 1990. He is currently a professor with the Department of Automation, Director of the System Integration Institute, and Director of the Networking Manufacturing Laboratory, Tsinghua University. From September 1993 to 1995, he was a visiting scientist, supported by Alexander von Humboldt Stiftung, with the Fraunhofer Institute for Production System and Design Technology (FHG/IPK), Germany. He has authored 10 books and published more than 300 research papers in journals and conferences. His research interests include enterprise modeling methods and optimization analysis, business process reengineering, workflow management, system integration, object-oriented technologies and flexible software systems, petri nets modeling and analysis, and workshop management and control.

Xiu Li, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

Xiu Li received the PhD degree in mechanical manufacturing and automation from Nanjing University of Aeronautics and Astronautics in 2000. She was once a visiting scientist at University of Hong Kong, the Hong Kong Polytechnic University and Georgia institute of technology. She is currently a professor with the Department of Information, Shenzhen Graduate School, Tsinghua University. She has published more than 100 papers in international transactions and conferences. Her research interests include intelligent systems, data mining and pattern recognition.

Liang Gu, Sangfor Technologies Inc., Shenzhen, China

Liang Gu received the PhD degree in Computer Software and Theory from Peking University in 2010. He worked as an associate research fellow at Yale university from 2010 to 2015. He is currently the chief scientist and the director of Sangfor Research Institute at Sangfor Technology Inc. As the person in charge of r&d technology at Sangfor, he is responsible for the technical framework improvement of a series of core products, including NGAF, AC, a Cloud HCI, aSAN and so on. These products have gained a leading market share in China and have been recognized by users and the market.

Cheng Wu, Tsinghua National Laboratory for Information Science and Technology, Beijing, China

Cheng Wu received the BS and MS degrees in electrical engineering from Tsinghua University, Beijing, China. He is currently a fellow of Chinese Academy of Engineering. His research interests include complex system modeling and optimization, and modeling and scheduling in supply chains.

Jia Zhang, Department of Electrical and Computer Engineering, Carnegie Mellon University, Silicon Valley, California, USA

Jia Zhang received the MS and BS degrees in computer science from Nanjing University, China and the PhD degree in computer science from the University of Illinois at Chicago. She is currently an associate professor at the Department of Electrical and Computer Engineering, Carnegie Mellon University. Her recent research interests center on service oriented computing, with a focus on collaborative scientific workflows, Internet of Things, cloud computing, and big data management. She has published more than 130 refereed journal papers, book chapters, and conference papers. She is currently an associate editor of the IEEE Transactions on Services Computing (TSC) and of International Journal of Web Services Research (JWSR), and editor-in-chief of International Journal of Services Computing (IJSC). She is a senior member of the IEEE.

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Published

2019-11-05

How to Cite

Gao, Z. ., Fan, Y. ., Li, X. ., Gu, . L. ., Wu, C. ., & Zhang, J. . (2019). Discovery and Analysis About the Evolution of Service Composition Patterns. Journal of Web Engineering, 18(7), 579–626. https://doi.org/10.13052/jwe1540-9589.1872

Issue

2019: Vol 18 Iss 7

Section

Articles