의사결정나무를 이용한 비즈니스 프로세스의 실시간 위험 수준 측정
Real-time Risk Measurement of Business Process Using Decision Tree
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초록
This paper proposes a methodology to measure the risk level in real-time for Business Activity Monitoring (BAM). A decision-tree methodology was employed to analyze the effect of process attributes on the result of the process execution. In the course of process execution, the level of risk is monitored in real-time, and an early warning can be issued depending on the change of the risk level. An algorithm for estimating the risk of ongoing processes in real-time was formulated. Comparison experiments were conducted to demonstrate the effectiveness of our method. The proposed method detects the risks of business processes more precisely and even earlier than existing approaches.
Abstract
This paper proposes a methodology to measure the risk level in real-time for Business Activity Monitoring (BAM). A decision-tree methodology was employed to analyze the effect of process attributes on the result of the process execution. In the course of process execution, the level of risk is monitored in real-time, and an early warning can be issued depending on the change of the risk level. An algorithm for estimating the risk of ongoing processes in real-time was formulated. Comparison experiments were conducted to demonstrate the effectiveness of our method. The proposed method detects the risks of business processes more precisely and even earlier than existing approaches.
참고문헌 (21)
- 손성호, "프로세스 기반 이벤트 분석을 이용한 비즈니스 활동 모니터링", 한국전자거래학회지, 12(2), pp.219~231 (2007), 한국전자거래학회
- Abadeh, M. S., "A parallel genetic local search algorithm for intrusion detection in computer networks", Engineering Applications of Artificial Intelligence, 20(8), pp.1058~1069 (2007)
- "Manufacturing Engineer", 2002, 282~283
- Adi, A., "Semantic Event Model and Its Implication on Situation Detection", Proceedings of the 2000 European Conference on Information (2000)
- Albaghdadi, M., "Event storm detection and identification in communication systems", Reliability Engineering and System Safety, 91(5), pp.602~613 (2006)
- Camci, F., "General support vector representation machine for one-class classification of non-stationary classes", Pattern Recognition, 41(10), pp.3021~3034 (2008)
- Duda, R. O., "Pattern Classification" (2001), Wiley-interscience
- Golfarelli, M., "Beyond data warehousing : what’s next in business intelligence?,", Proceedings of the 7th ACM international workshop on Data warehousing and OLAP, pp.1~6 (2004)
- Grigori, D., "Business Process Intelligence", Computers in Industry, 53, pp.321~343 (2004)
- Gross, P. N., "An Active Events Model for Systems Monitoring", Working Conference on Complex and Dynamic Systems (2001)
- Lamport, L., "Time, Clocks, and the Ordering of Events in a Distributed System", Communications of the ACM, 21(7), pp.558~565 (1978)
- Leymann, F., "Web Services and Business Process Management", IBM Systems Journal, 41(2) (2002)
- Luckham, D. C., "The Power of Events" (2002), Person Education
- Luckham, D. C., "Complex Event Processing in Distributed Systems" (1998), Stanford University
- Lundberg, A., "Leverage Complex Event Processing to Improve Operational Performance", Business Intelligence, 11(1), pp.55~65 (2006)
- Mendling, J., "Detection and prediction of errorsin EPCs of the SAP reference model", Data and Knowledge Engineering, 64(1), pp.312~329 (2008)
- Perrochon, L., "Event Mining with Event Processing Networks", Proceedings of Methodologies for Knowledge Discovery and Data Mining : Third Pacific-Asia Conference, 통권 : 1574, pp.474~478 (1999)
- Rolland, C.;, "“Modeling the Requirements Engineering Process,” 3rd European-Japanese Seminar on Information Modelling and Knowledge Bases", ~ (1993)
- Sharma, A., "Intrusion detection using text processing techniques with a kernel based similarity measure", Computers and Security, 26, pp.488~495 (2007)
- Wang D., "Robust multi-scale principal components analysis with applications to process monitoring", Journal of Process Control, 15(8), pp.869~882 (2005)
- Mitchell, T. M., "Machine Learning" (1997), McGraw-Hill, Inc.