标题:Towards Comprehensive Support for Privacy Preservation Cross-Organization Business Process Mining
作者:Liu, Cong; Duan, Hua; Zeng, QingTian; Zhou, MengChu; Lu, Faming; Cheng, Jiujun
作者机构:[Liu, Cong] Eindhoven Univ Technol, Dept Math & Comp Sci, NL-5611 Eindhoven, Netherlands.; [Liu, Cong; Duan, Hua; Zeng, QingTian; Lu, Faming] Shando 更多
通讯作者:Duan, H;Zeng, QT
通讯作者地址:[Duan, H; Zeng, QT]Shandong Univ Sci & Technol, Qingdao 266590, Shandong, Peoples R China.
来源:IEEE TRANSACTIONS ON SERVICES COMPUTING
出版年:2019
卷:12
期:4
页码:639-653
DOI:10.1109/TSC.2016.2617331
关键词:Business process privacy; business process management;; cross-organization process mining; Petri nets
摘要: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 privacy-preservation issue, which means the privacy of both event log and business process model. In this paper, a privacy-preservation cross-organization 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.
收录类别:SCIE
WOS核心被引频次:66
资源类型:期刊论文
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