한국전자거래학회지 (The Journal of Society for e-Business Studies)

  • 제15권4호
  • /
  • Pages.303-317
  • /
  • 2010
  • /
  • 2288-3908(pISSN)
  • /
  • 2765-3846(eISSN)
한국전자거래학회 (Society for e-Business Studies)
권호 표지

실시간 비즈니스 프로세스 모니터링 방법론을 위한 확장 KNN 대체 기반 LOF 예측 알고리즘

Extended KNN Imputation Based LOF Prediction Algorithm for Real-time Business Process Monitoring Method

  • 강복영 (서울대학교 산업공학과) ;
  • 김동수 (숭실대학교 산업정보시스템공학과) ;
  • 강석호 (서울대학교 산업공학과)
  • 투고 : 2010.11.16
  • 심사 : 2010.11.23
  • 발행 : 2010.11.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 KNN 대체와 LOF 알고리즘의 결합 모델을 확장하여 실시간 비즈니스 프로세스 모니터링을 위한 비정상 종료 예측 방법론을 제안하였다. 기존의 룰 기반 모니터링 방법론은 실시간 프로세스 진행 정도에 따른 비관측 정보에 기인하여 조기 경보 및 실시간 대응이 힘들다는 한계점을 안고 있다. 이를 해결하기 위하여 비관측 정보에 대한 가정 및 진행 중인 프로세스의 향후 경로 예측을 통해 종료 시점에서 예상되는 LOF를 추정하기 위한 알고리즘을 제안하였다. 이 알고리즘을 적용하여 실시간 비즈니스 프로세스 모니터링 과정에서 각 관측 시점마다 종료 시점에서의 결과를 예측함으로써, 전 시점에 걸친 추세를 살펴종료 패턴을 예측할 수 있다. 이를 통해 비즈니스 프로세스의 실시간 진척에 대한 정보를 가시화함으로써 기회 및 위협에 사전에 대응할 수 있게 하여 프로세스 관리 수준의 향상을 기대할 수 있을 것으로 예상된다.

In this paper, we propose a novel approach to fault prediction for real-time business process monitoring method using extended KNN imputation based LOF prediction. Existing rule-based approaches to process monitoring has some limitations like late alarm for fault occurrence or no indicators about real-time progress, since there exist unobserved attributes according to the monitoring phase during process executions. To improve these limitations, we propose an algorithm for LOF prediction by adopting the imputation method to assume unobserved attributes. LOF of ongoing instance is calculated by assuming next probable progresses after the monitoring phase, which is conducted during entire monitoring phases so that we can predict the abnormal termination of the ongoing instance. By visualizing the real-time progress in terms of the probability on abnormal termination, we can provide more proactive operations to opportunities or risks during the real-time monitoring.

키워드

참고문헌

  1. Breunig, M. M., Kriegel, H. P., Ng R. T. and Sander, J., "LOF : Identifying Density Based Local Outliers," In Proceedings of the ACM SIGMOD Conference, Dallas, TX, 2000.
  2. Buytendijk, F. and Flint, D., "How BAM can turn a business into a real-time enterprise," Gartner Research, AV-15-4650, 2002.
  3. Castellanos, M., Salazar, N., Casati, F., Dayal U. and Shan, M. C., "Predictive business operations management," International Journal of Computational Science and Engineering, Vol. 2, No. 5/6, 2006, pp. 292-301. https://doi.org/10.1504/IJCSE.2006.014772
  4. Chen, J. C. and Lin, K. Y., "Diagnosis for monitoring system of municipal solid waste incineration plant," Expert Systems with Applications, Vol. 34, No. 1, 2008, pp. 247-255. https://doi.org/10.1016/j.eswa.2006.09.002
  5. Curtis, B., Seshagiri, G. V., Reifer, D., Hirmanpour, I. and Keeni, G., "The cases for quantitative process management," IEEE software, Vol. 25, No. 3, 2008, pp. 24-28. https://doi.org/10.1109/MS.2008.80
  6. Ek, A. R., Robinson, A. P., Radtke, P. J. and Walters, D. K., "Development and testing of regeneration imputation models for forests in Minnesota," Forest Ecology and Management, Vol. 94, No. 1-3, 1997, pp. 129-140. https://doi.org/10.1016/S0378-1127(96)03970-9
  7. Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal M., and Shan, M. C., "Business process intelligence," Computers in Industry, Vol. 53, No. 3, 2004, pp. 321-343. https://doi.org/10.1016/j.compind.2003.10.007
  8. Grigori, D., Casati, F., Dayal U., and Shan, M. C., "Improving business process quality through exception understanding, prediction, and prevention," In Proceedings of the 27th Very Large Data Base Endowment Conference, Roma, Italy, 2001, pp. 159-168.
  9. Kang, B., Cho N. W. and Kang, S. H., "Real-time risk measurement for Business Activity Monitoring(BAM)," International Journal of Innovative Computing, Information and Control, Vol. 5, No. 11(A), 2009, pp. 3647-3657.
  10. Kang, B., Lee, S. K., Min, Y., Kang S. H. and Cho, N. W., "Real-time Process Quality Control for Business Activity Monitoring," In Proceedings of the 2009 International Conference on Computational Science and Its Applications, Yongin, Korea, 2009, pp. 237-242.
  11. Keung, P. and Kawalek, P., "Goalbased business process models: Creation and evaluation," Business Process Management Journal, Vol. 3 No. 1, 1997, pp. 17-38. https://doi.org/10.1108/14637159710161567
  12. Kim, K., Choi, I. and Park, C., "A rulebased approach to proactive exception handling in business processes," Expert Systems with Applications, Vol. 38, No. 1, 2011, pp. 394-409. https://doi.org/10.1016/j.eswa.2010.06.078
  13. Lazarevic, A., Ertoz, L., Ozgur, A., Srivastava, J. and Kumar, V., "A comparative study of anomaly detection schemes in network intrusion detection," In Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, 2003.
  14. Leitner, P., Wetzstein, B., Rosenberg, F., Michlmayr, A., Dustdar S., and Leymann, F., "Runtime prediction of service level agreement violations for composite services," In Proceedings of the 3rd Workshop on Non-Functional Properties and SLA Management in Service-Oriented Computing, Stockholm, Sweden, 2009.
  15. Medioni, G., Cohen, I., Hongeng, S., Bremond F., and Nevatia. R., "Event Detection and Analysis from Video Streams," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 8, No. 23, 2001, pp. 873-889.
  16. Nie, G., Zhang, L., Liu, Y., Zheng, X. and Shi, Y., "Decision analysis of data mining project based on Bayesian risk," Expert Systems with Applications, Vol. 36, No. 3, 2009, pp. 4589-4594. https://doi.org/10.1016/j.eswa.2008.05.014
  17. Pokrajac, D., Lazarevic, A. and Latecki, L. J., "Incremental Local Outlier Detection for Data Streams," IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2007.
  18. Rao, U. S., Kestur, S. and Pradhan, C., "Stochastic optimization modeling and quantitative project management," IEEE software, Vol. 25, No. 3, 2008, pp. 29-36.
  19. Rusinov, L. A., Rudakova, I. V., and Kurkina, V. V., "Real time diagnostics of technological processes and field equipment," Chemometrics and Intelligent Laboratory Systems, Vol. 88, No. 1, 2007, pp. 18-25. https://doi.org/10.1016/j.chemolab.2006.11.007
  20. Viaene, S., Dedene, G. and Derrig, R. A., "Auto claim fraud detection using Bayesian learning neural networks," Expert Systems with Applications, Vol. 29, No. 3, 2005, pp. 653-666. https://doi.org/10.1016/j.eswa.2005.04.030
  21. Wang, D. and Romagnoli, J. A., "Robust multi-scale principal components analysis with applications to process monitoring," Journal of Process Control, Vol. 15, No. 8, 2005, pp. 869-882. https://doi.org/10.1016/j.jprocont.2005.04.001
  22. Yue, D., Wu, X., Wang, Y., Li, Y. and Chu, C. H., "A Review of Data Mining-Based Financial Fraud Detection Research," In Proceedings of 2007 International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, China, 2007, pp. 5514-5517.