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Using Classification Method for Querying the Relevant Process Models

  • Jiaxing Wang
  • Sibin Gao
  • Hongjie Peng
  • Bin Cao
  • Jing FanEmail author
Conference paper
  • 416 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 602)

Abstract

Operations management is important to a company, so more and more business process models are created. At the same time, how to manage such a large amount of process models is becoming a big challenge for companies. Querying the relevant process models is proposed as a business process management technology and it has attracted more and more attention by researchers. The existing methods query the relevant models for a query process model by measuring their similarities. And most of them measure the similarity by focusing on only one kind of feature, such as the structural features or behavioral features, while ignoring other features. In this paper, we consider both structural features and behavioral features to query the relevant process models for a query process model. In order to reach this goal, we use two classification methods named back propagation neural network (BPNN) and support vector machines(SVM) for classifying the candidate models in the repository into two classes: relevant and irrelevant. For the sake of classification, we summarize 7 features to represent the similar or dissimilar parts of two process models. The experiment result shows the precision and efficiency of the classification methods are acceptable.

Keywords

Business process model BPNN SVM Relevant process model Classification 

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Jiaxing Wang
    • 1
  • Sibin Gao
    • 1
  • Hongjie Peng
    • 1
  • Bin Cao
    • 1
  • Jing Fan
    • 1
    Email author
  1. 1.College of Computer ScienceZhejiang University of TechnologyHangzhouChina

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