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A BPM Activity-Performer Correspondence Analysis Method

BPM 기반의 업무-수행자 대응분석 기법

  • Ahn, Hyun (Department of Computer Science, Kyonggi University) ;
  • Park, Chungun (Department of Mathematics, Kyonggi University) ;
  • Kim, Kwanghoon (Department of Computer Science, Kyonggi University)
  • Received : 2013.05.16
  • Accepted : 2013.07.27
  • Published : 2013.08.31

Abstract

Business Process Intelligence (BPI) is one of the emerging technologies in the knowledge discovery and analysis area. BPI deals with a series of techniques from discovering knowledge to analyzing the discovered knowledge in BPM-supported organizations. By means of the BPI technology, we are able to provide the full functionality of control, monitoring, prediction, and optimization of process-supported organizational knowledge. Particularly, we focus on the focal organizational knowledge, which is so-called the BPM activity-performer affiliation networking knowledge that represents the affiliated relationships between performers and activities in enacting a specific business process model. That is, in this paper we devise a statistical analysis method to be applied to the BPM activity-performer affiliation networking knowledge, and dubbed it the activity-performer correspondence analysis method. The devised method consists of a series of pipelined phases from the generation of a bipartite matrix to the visualization of the analysis result, and through the method we are eventually able to analyze the degree of correspondences between a group of performers and a group of activities involved in a business process model or a package of business process models. Conclusively, we strongly expect the effectiveness and efficiency of the human resources allotments, and the improvement of the correlational degree between business activities and performers, in planning and designing business process models and packages for the BPM-supported organization, through the activity-performer correspondence analysis method.

비즈니스 프로세스 인텔리전스(BPI)는 지식의 발견 및 분석 분야의 새로운 기술로서, BPM 기반 조직에 관련된 지식을 발견하고 이를 분석하기 위한 기술들을 말한다. BPI를 통해, 프로세스 기반 조직의 지식을 제어, 모니터링, 예측, 최적화할 수 있게 되는데, 본 논문에서는 특정 비즈니스 프로세스 모델에 참여하는 수행자들과 업무들간의 소속 관계를 나타내는 BPM 업무-수행자 소속성 네트워크 지식에 초점을 맞춘다. 즉, 본 논문에서는 BPM 업무-수행자 소속성 네트워크 지식을 위한 통계 분석 기법을 제안하며, 이를 업무-수행자 대응 분석 기법이라 정의한다. 제안하는 대응 분석 기법의 과정은 이분 행렬을 생성하고, 이에 대한 대응 분석 결과를 가시화하는 과정으로 구성되며, 이를 통해 비즈니스 프로세스 모델 또는 비즈니스 프로세스 패키지에 소속되는 수행자 그룹과 업무 그룹간의 연관 관계를 분석할 수 있다. 결론적으로, 제안하는 업무-수행자 대응 분석 기법을 통해 BPM 기반 조직을 위한 비즈니스 프로세스 모델 또는 비즈니스 프로세스 패키지의 계획 및 설계 과정에서, 업무와 수행자간의 연관 관계를 고려하여, 인적 자원 할당의 효과성과 효율성을 제고할 것이라 기대된다.

Keywords

Acknowledgement

Supported by : 중소기업청

References

  1. F. Leymann, D. Roller, Production Workflow: Concepts and Techniques, Prentice Hall PTR, 1999.
  2. C. A. Ellis, G. J. Nutt, "Office Information Systems and Computer Science," ACM Computing Surveys, Vol. 12, No. 1, pp.27-60, 1980. https://doi.org/10.1145/356802.356805
  3. K. Kim, "Workflow Technology II: Workflow Management Technology and Business Process Management Technology," TTA Journal, No. 87, pp.120-133, 2003.
  4. K. Kim, "Business Process Management System," Korea Information Processing Society Review, Vol. 12, No, 3, pp.45-56, 2005.
  5. W. M. P. van der Aalst, A. J. M. M. Weijters, "Process Mining: A Research Agenda," Journal of Computers in Industry, Vol. 53, No. 3, pp.231-244, 2004. https://doi.org/10.1016/j.compind.2003.10.001
  6. M. Park, K. Kim, "Control-path Oriented Workflow Intelligence Analyses," Journal of Information Science and Engineering, Vol. 24, No.2, pp.343-359, 2008.
  7. M. Park, K. Kim, "A Workflow Evnet Logging Mechanism and Its Implications on Quality of Workflows," Journal of Information Science and Engineering, Vol. 26, No. 5, pp.1817-1830, 2010.
  8. D. Grigori, et al., "Business Process Intelligence," Journal of Computer in Industry, Vol. 53, No. 3, pp.321-343, 2004. https://doi.org/10.1016/j.compind.2003.10.007
  9. J. Schiefer, et al., "Process Information Factory: A Data Management Approach for Enhancing Business Process Intelligence," Proceedings of the IEEE International Conference on e-Commerce Technology, pp.162-169, 2004.
  10. W. Tan, et al., "A Business Process Intelligence System for Enterprise Process Performance Management," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 38, No. 6, pp.745-756, 2008. https://doi.org/10.1109/TSMCC.2008.2001571
  11. K. Faust, "Centrality in Affiliation Networks," Journal of Social Networks, Vol. 19, No. 2, pp.157-191, 1997. https://doi.org/10.1016/S0378-8733(96)00300-0
  12. S. P. Borgatti, D. S. Halgin, "Analyzing Affiliation Networks," The Sage Handbook of Social Network Analysis, pp.417-433, 2010.
  13. H. Kim, H. Ahn, K. Kim, "A Workflow Affiliation Network Discovery Algorithm," ICIC Express Letters, Vol. 6, No. 3, pp.765-770, 2012.
  14. K. Kim, "Discovering Activity-Performer Affiliation Knowledge on ICN-based Workflow Models," Journal of Information Science and Engineering, Vol. 29, No. 1, pp.79-97, 2013.
  15. H. Ahn, K. Kim, "An Activity-Performer Bipartite Matrix Generation Algorithm for Analyzing Workflow-supported Human-Resource Affiliations," Journal of Korean Society for Internet Information (accepted), 2013.
  16. A. Agresti, Categorical Data Analysis Introduction, Translated by K. Jeong, Y. Choi, 2009.
  17. W. M. P. van der Aalst, et al., "Discovering Social Networks from Event Logs," Journal of Computer Supported Cooperative Work, Vol. 14, No. 6, pp. 549-593, 2005. https://doi.org/10.1007/s10606-005-9005-9
  18. K. Kim, "A Workflow-based Social Network Discovery and Analysis System," Proceedings of the 1st International Symposium on Data-driven Process Discovery and Analysis, pp. 163-176, 2011.
  19. K. Kim, C.A. Ellis, "ICN-Based Workflow Model and Its Advances," Handbook of Research on Business Process Modelling, pp.34-54, 2009.