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An Adaptive Business Process Mining Algorithm based on Modified FP-Tree  

Kim, Gun-Woo (한양대학교 컴퓨터공학과)
Lee, Seung-Hoon (한양대학교 컴퓨터공학과)
Kim, Jae-Hyung (알티베이스 DBMS R&D 개발 본부)
Seo, Hye-Myung (한양대학교 분자생물학과)
Son, Jin-Hyun (한양대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.3, 2010 , pp. 301-315 More about this Journal
Abstract
Recently, competition between companies has intensified and so has the necessity of creating a new business value inventions has increased. A numbers of Business organizations are beginning to realize the importance of business process management. Processes however can often not go the way they were initially designed or non-efficient performance process model could be designed. This can be due to a lack of cooperation and understanding between business analysts and system developers. To solve this problem, business process mining which can be used as the basis of the business process re-engineering has been recognized to an important concept. Current process mining research has only focused their attention on extracting workflow-based process model from competed process logs. Thus there have a limitations in expressing various forms of business processes. The disadvantage in this method is process discovering time and log scanning time in itself take a considerable amount of time. This is due to the re-scanning of the process logs with each new update. In this paper, we will presents a modified FP-Tree algorithm for FP-Tree based business processes, which are used for association analysis in data mining. Our modified algorithm supports the discovery of the appropriate level of process model according to the user's need without re-scanning the entire process logs during updated.
Keywords
Process Mining; Data Mining; Business Process;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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