Towards comprehensive support for privacy preservation cross-organization business process mining

Cong Liu, Hua Duan, Qing Tian Zeng, Meng Chu Zhou, Faming Lu, Jiujun Cheng

Research output: Contribution to journalArticlepeer-review

Abstract

More and more business requirements are crossing organizational boundaries. There comes the cross-organization business process management, and its modeling is a complicated task. Mining a cross-organization business process aims to discover its model from a set of distributed event logs. Unfortunately, traditional process mining approaches totally neglect the privacypreservation issue, which means the privacy of both event log and business process model. In this paper, a privacy-preservation crossorganization business process mining framework is proposed to handle its privacy issues. It includes three steps: (1) each organization discovers its private and public business process models from its event logs; (2) the trusted third-party midware takes the public process models as input and generates cooperative public process model fragments of each organization; and (3) each organization combines its private business process model with its relevant public fragments to obtain the organization-specific cross-organization cooperative business process model. To illustrate the applicability of the proposed approach, a multi-modal cross-organization transportation case is used for its validation and comparison with other methods.

Original languageEnglish (US)
Article number2617331
Pages (from-to)639-653
Number of pages15
JournalIEEE Transactions on Services Computing
Volume12
Issue number4
DOIs
StatePublished - Jul 1 2019

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management

Keywords

  • Business process management
  • Business process privacy
  • Cross-organization process mining
  • Petri nets

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