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Big Data-Based Similarity Network Model for Cloud Manufacturing Services

In: Intelligent Engineering and Management for Industry 4.0

Author

Listed:
  • Qian Zhang

    (College of Mechanical Engineering, Chongqing University)

  • Peihan Wen

    (College of Mechanical Engineering, Chongqing University)

  • Pan Wang

    (College of Mechanical Engineering, Chongqing University)

  • Jawad Ul Hassan

    (College of Mechanical Engineering, Chongqing University)

Abstract

Almost all key techniques of Cloud Manufacturing (CMfg) refer to services and combination of services, which make it urgent to deeply explore intrinsic characteristics together with evolution rules of relationships between services. As a critical topic of CMfg, service combination and optimal selection (SCOS) will also benefit from the exploration. Hence, the feature of similarity between services is studied with the method of network analysis and applied in services importance evaluation and clustering. A similarity evaluation method for CMfg services is proposed firstly based on service invocation history. Then, a service similarity network model is established and visualized by means of a similarity adjacency matrix. Furthermore, importance of each service is evaluated by three characteristics of the service similarity network model, i.e., degree, eigenvector centrality, and clustering coefficient. A case study validates the feasibility and effectiveness of the proposed similarity network model together with related similarity evaluation method.

Suggested Citation

  • Qian Zhang & Peihan Wen & Pan Wang & Jawad Ul Hassan, 2022. "Big Data-Based Similarity Network Model for Cloud Manufacturing Services," Springer Books, in: Yong-Hong Kuo & Yelin Fu & Peng-Chu Chen & Calvin Ka-lun Or & George G. Huang & Junwei Wang (ed.), Intelligent Engineering and Management for Industry 4.0, pages 35-43, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-94683-8_4
    DOI: 10.1007/978-3-030-94683-8_4
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