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.

References

V. Andrikopoulos, S. Benbernou, and M. P. Papazoglou (2012), On the evolution of services, IEEE Transactions on Software Engineering, Vol. 38, no. 3, pp. 609–628.

X. Liu, Y. Hui, W. Sun, and H. Liang (2007), Towards service composition based on mashup, in Proceedings of IEEE World Congress on Services (SERVICES), pp. 332–339.

M. Yamashita, K. Becker, and R. Galante (2011), Service evolution management based on usage profile, in Proceedings of IEEE International Conference on Web Services (ICWS), pp. 746–747.

M. Yamashita, B. Vollino, K. Becker, and R. Galante (2012), Measuring change impact based on usage profiles, in Proceedings of IEEE International Conference on Web Services, pp. 226–233.

M. Fokaefs, R. Mikhaiel, N. Tsantalis, E. Stroulia, and A. Lau (2011), An empirical study on web service evolution, in Proceedings of IEEE International Conference on Web Services (ICWS), pp. 49–56.

D. Romano and M. Pinzger (2012), Analyzing the evolution of web services using fine-grained changes, in Proceedings of IEEE International Conference on Web Services (ICWS), 2012, pp. 392–399.

S. Wang and M. A. Capretz (2009), A dependency impact analysis model for web services evolution, in Proceedings of International Conference on Web Services (ICWS), pp. 359–365.

S. Wang, W. A. Higashino, M. Hayes, and M. A. M. Capretz (2014), Service evolution patterns, in Proceedings of IEEE International Conference on Web Services (ICWS), pp. 201–208.

Z. Gao, Y. Fan, C. Wu, W. Tan, J. Zhang, Y. Ni, B. Bai, and S. Chen (2016), SeCo-LDA: mining service co-occurrence topics for recommendation, in Proceedings of IEEE International Conference on Web Services (ICWS), pp. 25–32.

D. M. Blei and J. D. Lafferty (2006), Dynamic topic models, in Proceedings of ACM International Conference on Machine Learning (ICML), pp. 113–120.

D. Blei and J. Lafferty (2007), A correlated topic model of science, the Annals of Applied Statistics, no. 1.1, pp. 1735.

Y. Zhong, Y. Fan, K. Huang, W. Tan, and J. Zhang (2014), Timeaware service recommendation for mashup creation in an evolving service ecosystem, in Proceedings of IEEE International Conference on Web Services (ICWS), pp. 25–32.

K. C. Bhardwaj and R. K. Sharma (2015), Machine learning in efficient and effective web service discovery, J. Web Engineering Vol. 14, pp. 196–214.

S. Kamath and V. S. Ananthanrayana (2016), Semantic similarity based context-aware web service discovery using nlp techniques, J. Web Engineering, Vol. 15, pp. 110–139.

Z. Gao, Y. Fan, C. Wu, W. Tan and J. Zhang (2017), Service recommendation from the evolution of composition patterns, in Proceedings of IEEE International Conference on Services Computing (SCC), pp. 108–115.

X. Wang, C. Zhai, and D. Roth (2013), Understanding evolution of research themes: a probabilistic generative model for citations, in Proceedings of the ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1115–1123.

B. Tapia, R. Torres, H. Astudillo, and P. Ortega (2011), Recommending APIs for mashup completion using association rules mined from real usage data, in Computer Science Society, pp. 83–89.

P. L. F. da Silva, L. O. B. Santos and E. G. da Silva (2009), Towards a goalbased service framework for dynamic service discovery and composition, in Proceedings of IEEE International Conference on Information Technology: New Generations, pp. 302–307.

D. Zhovtobryukh (2007), A petri net-based approach for automated goal-driven web service composition, Simulation, Vol. 83, no. 1, pp. 33–63.

D. M. Blei, A. Y. Ng, and M. I. Jordan (2003), Latent dirichlet allocation, the Journal of machine Learning research, Vol. 3, pp. 993–1022.

Y. Jo, J. E. Hopcroft, and C. Lagoze (2011), The web of topics: discovering the topology of topic evolution in a corpus, in Proceedings of International Conference on World Wide Web (WWW), pp. 257–266.

T. M. J. Fruchterman and E. M. Reingold (1991), Graph drawing by force-directed placement, Software Practice and Experience, Vol. 21, no. 11, pp. 1129–1164.

A. P. Barros and M. Dumas (2006), The rise of web service ecosystems, IT professional, no. 5, pp. 31–37.

E. Al-Masri and Q. H. Mahmoud (2008), Investigating web services on the World Wide Web, in Proceedings of International Conference on World Wide Web (WWW), pp. 795–804.

Y. Yue, T. Finley, F. Radlinski, and T. Joachims (2007), A support vector method for optimizing average precision, in Proceedings of the 30th International Conference on Research and Development in Information Retrieval, pp. 271–278.

K. Goarany, G. Kulczycki, and M. B. Blake (2010), Mining social tags to predict mashup patterns, in Proceedings of ACM International Workshop on Search and Mining User-generated Contents (SMUC), pp. 71–78.

R. Agrawal, R. Srikant et al. (1994), Fast algorithms for mining association rules, in Proceedings of 20th International Conference on Very Large Data Base (VLDB), Vol. 1215, pp. 487–499.

C. Li, R. Zhang, J. Huai, X. Guo, and H. Sun (2013), A probabilistic approach for web service discovery, in Proceedings of IEEE International Conference on Services Computing (SCC), pp. 49–56.

Y. Zhang, T. Lei, and Y. Wang (2016), A service recommendation algorithm based on modeling of implicit demands, in Proceedings of IEEE International Conference on Web Services (ICWS), pp. 17–24.

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Published

2019-11-05

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