A Recommendation System to Facilitate Business Process Modeling

Shuiguang Deng, Dongjing Wang, Ying Li, Bin Cao, Jianwei Yin, Zhaohui Wu, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.

Original languageEnglish (US)
Article number7447788
Pages (from-to)1380-1394
Number of pages15
JournalIEEE Transactions on Cybernetics
Volume47
Issue number6
DOIs
StatePublished - Jun 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Pattern extraction
  • process mining
  • process modeling
  • process recommendation

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