1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Towards comprehensive support for organizational mining Presenter : Yu-hui Huang Authors : Minseok Song, Wil M.P. van der Aalst DSS 2008 國立雲林科技大學 National Yunlin University of Science and Technology 1

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiment Conclusion

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation Today event logs are widely available and growing. we can constructing a process flow by analyze the even log and improve it.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective The primary goal of process mining is to extract knowledge from these logs and use it for a detailed analysis of reality To discover organizational models and social networks from the process log.

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Process mining ： it is to extract information from event logs.

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Process model ： Organization model ：

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Process log ：

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Organizational model markup language ：

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Organizational mining ： (1)organizational model mining  Task-base: similar skills and knowledge to perform the tasks  Default mining ：  Metrics based ：  Agglomerative Hierarchical Clustering (AHC) ：  Case-base: different skills and work together  Metrics based on joint cases ： (2)social network analysis (3)information flows

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Metrics based (task-base) ：

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology AHC (task-base ) ：

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Metrics based on joint cases ：

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Social network analysis ： Information flows between organizational entities

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment

17. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 17 To evaluate the organizational model mining results, conformance test methods should be developed. We can apply non-disjoint clustering methods to reflect an organization in which originators play multiple roles.

18. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 18 Comments Advantage  … Drawback  …. Application  …