Journal of KIISE:Computing Practices and Letters (한국정보과학회논문지:컴퓨팅의 실제 및 레터)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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An Adaptive Business Process Mining Algorithm based on Modified FP-Tree

변형된 FP-트리 기반의 적응형 비즈니스 프로세스 마이닝 알고리즘

  • 김건우 (한양대학교 컴퓨터공학과) ;
  • 이승훈 (한양대학교 컴퓨터공학과) ;
  • 김재형 (알티베이스 DBMS R&D 개발 본부) ;
  • 서혜명 (한양대학교 분자생물학과) ;
  • 손진현 (한양대학교 컴퓨터공학과)
  • Received : 2009.01.14
  • Accepted : 2009.12.28
  • Published : 2010.03.15
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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.

기업 간의 경쟁이 심화되고 새로운 비즈니스 가치 창출을 위한 필요성이 증대되고 있는 상황에서, 기업들은 비즈니스 프로세스 관리 기술에 많은 관심을 기울이고 있다. 하지만 비즈니스 분석가와 시스템 개발자간의 이해 정도 및 의견 불일치 등으로 인하여 프로세스가 의도한대로 실행되지 않거나 효율이 떨어지는 프로세스 등이 설계될 수 있다. 이러한 문제점을 해결하기 위하여 비즈니스 프로세스 재설계의 근거로 사용될 수 있는 비즈니스 프로세스 마이닝이 중요한 개념으로 인식되고 있다. 하지만 기존의 프로세스 마이닝에 관한 연구에서는 완성되어 있는 프로세스 로그를 기반으로 워크플로우 기반의 프로세스 모델을 추출하는 단조로운 형태였기 때문에 다양한 형태의 비즈니스 프로세스를 표현하는데 한계가 있었으며, 새로운 프로세스 로그가 추가될 때마다 로그 정보들을 재 스캔해야함으로 프로세스 검출 및 로그정보 탐색시간이 느려지는 단점이 존재하였다. 본 논문에서는 데이터 마이닝의 연관성 분석에 사용되는 FP-트라를 비즈니스 프로세스에 적합하게 변형하여 추가되는 대량의 프로세스 로그 정보를 재 스캔과정 없이 사용자가 원하는 수준의 프로세스 모델을 검출하도록 지원하는 변형된 FP-트리 기반의 프로세스 마이닝 알고리즘을 제시하고자 한다.

Keywords

References

  1. W.M.P. van der Aalst, A.J.M.M. Weijters, "Process Mining: A Research Agenda," Computers in Industry, vol.53, no.3, pp.231-244, 2004. https://doi.org/10.1016/j.compind.2003.10.001
  2. W.M.P. van der Aalst, M. Dumas, C. Ouyang, A. Rozinat, H.M.W. Verbeek, "Conformance Checking of Service Behavior," ACM Transactions on Internet Technology (TOIT), vol.8, no.3, 2008.
  3. W.M.P. van der Aalst, "Trends in Business Process Analysis: From Verification to Process Mining," Proceedings of the 9th International Conference on Enterprise Information Systems (ICEIS 2007), pp.12-22, 2007.
  4. XIE Yi-wu, LI Xiao-wan, Chen Yan, "The Research on the Usage of Business Process Mining in the Implementation of BPR," Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops, pp.995-1000, 2007.
  5. J. Han, J. Pei, and Y. Yin, "Mining frequent patterns without condidate generation," Proceedings of 2000 ACM SIGMOD Int. Conf. Management of Data(SIGMOD'00), Dallas, Tx, pp.1-12, 2000.
  6. A. Tiwari, C.J. Turner, B. Majeed, "A review of business process mining : state-of-the-art and future trends," Business Process Management Journal, vol.14, no.1, pp.5-22, 2008. https://doi.org/10.1108/14637150810849373
  7. W.M.P. van der Aalst, "Trends In Business Process Analysis : From Verification to Process Mining," Proceedings of the 9th International Conference on Enterprise Information Systems (ICEIS 2007), pp.12-22, 2007.
  8. W.M.P. van der Aalst and C.W. Gunther, "Finding Structure in Unstructured Processes: The Case for Process Mining," Proceedings the 7th International Conference on Applications of Concurrency to System Design, ACSD 2007, pp.3-12, 2007.
  9. A.K. Medeiros, A.J.M.M. Weijters, W.M.P. van der Aalst, "Genetic process mining: an experimental evaluation," Journal of Data Mining and Knowledge Discovery, vol.14, no.2, pp.245-304, 2007. https://doi.org/10.1007/s10618-006-0061-7
  10. Object Management Group/Business Process Management Initiative, "BPMN 1.1: OMG Specification," February, 2008.
  11. A.K. Medeiros, Antonella Guzzo, Gianluigi Greco, W.M.P. van der Aalst, A.J.M.M. Weijters, Boudewijn F. van Dongen, Domenico Sacca, "Process Mining Based on Clustering: A Quest for Precision," BPM Workshops, LNCS 4928, pp.17-29, 2008.
  12. S. Chung, S. Kwon, "A Process Mining using Association Rule and Sequence Pattern(in korean)," Journal of the Society of Korea Industrial and Systems Engineering, vol.31, no.2, pp.104-111, 2008.
  13. M. Funk, A. Rozinat, A.K. Medeiros, P.H.A. van der Putten, H. Corporaal, W.M.P. van der Aalst, "Semantic Concepts in Product Usage Monitoring and Analysis," ESR-2008-10, Report of Group of Electronics Systems, Department of Electrical Engineering, TU/e, 2008.
  14. A. Rozinat, M. Veloso, W.M.P. van der Aalst "Evaluating the Quality of Discovered Process Models," Proceedings of Induction of Process Models, IPM workshop, pp.45-52, 2008.
  15. A. Rozinat, R.S. Mans, M. Song, W.M.P. van der Aalst, "Discovering Simulation Models, BETA Working Paper Series," WP 223, Eindhoven University of Technology, Eindhoven, 2007.