Soft Computing

, Volume 22, Issue 2, pp 645–657 | Cite as

Efficient business process consolidation: combining topic features with structure matching

  • Ying HuangEmail author
  • Wei Li
  • Zhengping Liang
  • Yu Xue
  • Xiuni Wang
Methodologies and Application


Accurate and effective business process consolidation is an efficient means of overcoming the dynamics and uncertainty in business process modeling. This article presents an approach to automating business process consolidation by applying process topic clustering based on business process libraries, using a graph mining algorithm to extract process patterns, identifying frequent subgraphs under the same process topic, filling the pertinent subgraph information into a table of frequent process subgraphs, and finally merging these frequent subgraphs to obtain merged business processes using a process merging algorithm. Tests on 604 models from the SAP reference model were performed, in which we used the compression ratio to judge the capability of our merging methods; the compression ratios of integrated processes in the same topic cluster were found to be much lower than those of processes related to different topics, and our method was found to achieve compression ratios similar to those reported in previous work.


Correlated topic model Topic distillation Business process merging gSpan Process subgraph 



This work was supported by the National Natural Science Foundation of China under Grant Nos. 61573157, 61562038 and 61562703, the Natural Science Foundation of Jiangxi Province under Grant No. 20142BAB217028, the Science and Technology Planning Project of Guangdong Province of China under Grant No. 2015B010129015.

Compliance with ethical standards

Conflict of interest

The authors declare there is no conflict of interests regarding the publication of this paper.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Ying Huang
    • 1
    • 2
    Email author
  • Wei Li
    • 3
    • 4
  • Zhengping Liang
    • 5
  • Yu Xue
    • 6
  • Xiuni Wang
    • 7
  1. 1.Institute of Mathematical and Computer SciencesGannan Normal UniversityGanzhouPeople’s Republic of China
  2. 2.State Key Laboratory of Software Engineering, Computer SchoolWuhan UniversityWuhanPeople’s Republic of China
  3. 3.College of Mathematics and InformaticsSouth China Agricultural UniversityGuangzhouPeople’s Republic of China
  4. 4.School of Information EngineeringJiangxi University of Science and TechnologyGanzhouPeople’s Republic of China
  5. 5.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenPeople’s Republic of China
  6. 6.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  7. 7.School of Computer ScienceGuangzhou UniversityGuangzhouPeople’s Republic of China

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