A graph distance based metric for data oriented workflow retrieval with variable time constraints

  • Authors:

  • Yinglong Ma;Xiaolan Zhang;Ke Lu

  • Affiliations:

  • School of Control and Computer Science, North China Electric Power University, Beijing 102206, China and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Science ...;School of Control and Computer Science, North China Electric Power University, Beijing 102206, China;University of Chinese Academy of Sciences, Beijing 100049, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2014

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Abstract

There are many applications in business process management that require measuring the similarity between business processes, such as workflow retrieval and process mining, etc. However, most existing approaches and models cannot represent variable constraints and achieve data oriented workflow retrieval of considering different QoS requirements, and also fail to allow users to express arbitrary constraints based on graph structures of workflows. These problems will impede the customization and reuse of workflows, especially for data oriented workflows. In this paper, we will be towards workflow retrieval with variable time constraints. We propose a graph distance based approach for measuring the similarity between data oriented workflows with variable time constraints. First, a formal structure called Time Dependency Graph (TDG) is proposed and further used as representation model of workflows. Similarity comparison between two workflows can be reduced to computing the similarity between their TDGs. Second, we detect whether two TDGs of workflows for similarity comparison are compatible. A distance based measure is proposed for computing their similarity by their normalization matrices established based on their TDGs. We theoretically proof that the proposed measure satisfies the all the properties of distance. In addition, some exemplar processes are studied to illustrate the effectiveness of our approach of similarity comparison for workflows.