Mining Social Networks: Uncovering Interaction Patterns in Business Processes

  • Wil M. P. van der Aalst
  • Minseok Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3080)

Abstract

Increasingly information systems log historic information in a systematic way. Workflow management systems, but also ERP, CRM, SCM, and B2B systems often provide a so-called “event log”, i.e., a log recording the execution of activities. Unfortunately, the information in these event logs is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. This paper focuses on the mining social networks. This is possible because event logs typically record information about the users executing the activities recorded in the log. To do this we combine concepts from workflow management and social network analysis. This paper introduces the approach, defines metrics, and presents a tool to mine social networks from event logs.

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References

  1. 1.
    van der Aalst, W.M.P., van Dongen, B.F.: Discovering Workflow Performance Models from Timed Logs. In: Han, Y., Tai, S., Wikarski, D. (eds.) EDCIS 2002. LNCS, vol. 2480, pp. 45–63. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., van Hee, K.M.: Workflow Management: Models, Methods, and Systems. MIT Press, Cambridge (2002)Google Scholar
  3. 3.
    van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering 47(2), 237–267 (2003)CrossRefGoogle Scholar
  4. 4.
    van der Aalst, W.M.P., Weijters, A.J.M.M. (eds.): Process Mining, Special Issue of Computers in Industry, vol. 53(3). Elsevier Science Publishers, Amsterdam (2004)Google Scholar
  5. 5.
    van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: WorkflowMining: Discovering Process Models from Event Logs. QUT Technical report, FIT-TR-2003-03, Queensland University of Technology, Brisbane (2003) (Accepted for publication in IEEE Transactions on Knowledge and Data Engineering)Google Scholar
  6. 6.
    Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Sixth International Conference on Extending Database Technology, pp. 469–483 (1998)Google Scholar
  7. 7.
    Bavelas, A.A.: A Mathematical Model for Group Structures. Human Organization 7, 16–30 (1948)Google Scholar
  8. 8.
    Bernard, H.R., Killworth, P.D., McCarty, C., Shelley, G.A., Robinson, S.: Comparing Four Different Methods for Measuring Personal Social Networks. Social Networks 12, 179–216 (1990)CrossRefGoogle Scholar
  9. 9.
    Burt, R.S., Minor, M.: Applied Network Analysis: A Methodological Introduction. Sage, Newbury Park (1983)Google Scholar
  10. 10.
    Cook, J.E., Wolf, A.L.: Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology 7(3), 215–249 (1998)CrossRefGoogle Scholar
  11. 11.
    Feldman, M.: Electronic mail and weak ties in organizations. Office: Technology and People 3, 83–101 (1987)CrossRefGoogle Scholar
  12. 12.
    Freeman, L.C.: A Set of Measures of Centrality Based on Betweenness. Sociometry 40, 35–41 (1977)CrossRefGoogle Scholar
  13. 13.
    Freeman, L.C.: Centrality in Social Networks: Conceptual Clarification. Social Networks 1, 215–239 (1979)CrossRefGoogle Scholar
  14. 14.
    Grigori, D., Casati, F., Dayal, U., Shan, M.C.: Improving Business Process Quality through Exception Understanding, Prediction, and Prevention. In: Apers, P., Atzeni, P., Ceri, S., Paraboschi, S., Ramamohanarao, K., Snodgrass, R. (eds.) Proceedings of 27th International Conference on Very Large Data Bases (VLDB 2001), pp. 159–168. Morgan Kaufmann, San Francisco (2001)Google Scholar
  15. 15.
    Herbst, J.: A Machine Learning Approach toWorkflowManagement. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 183–194. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  16. 16.
    IDS Scheer. ARIS Process Performance Manager (ARIS PPM) (2002), http://www.idsscheer.com
  17. 17.
    Moreno, J.L.: Who Shall Survive? Nervous and Mental Disease Publishing Company, Washington (1934)Google Scholar
  18. 18.
    zur Mühlen, M., Rosemann, M.: Workflow-based Process Monitoring and Controlling - Technical and Organizational Issues. In: Sprague, R. (ed.) Proceedings of the 33rd Hawaii International Conference on System Science (HICSS-33), pp. 1–10. IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  19. 19.
    Nemati, H., Barko, C.D.: Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance. Idea Group Publishing, Hershey (2003)Google Scholar
  20. 20.
    Reisig, W., Rozenberg, G. (eds.): APN 1998. LNCS, vol. 1491. Springer, Heidelberg (1998)MATHGoogle Scholar
  21. 21.
    Sayal, M., Casati, F., Shan, M.C., Dayal, U.: Business Process Cockpit. In: Proceedings of 28th International Conference on Very Large Data Bases (VLDB 2002), pp. 880–883. Morgan Kaufmann, San Francisco (2002)CrossRefGoogle Scholar
  22. 22.
    Scott, J.: Social Network Analysis. Sage, Newbury Park CA (1992)Google Scholar
  23. 23.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)Google Scholar
  24. 24.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering 10(2), 151–162 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Wil M. P. van der Aalst
    • 1
  • Minseok Song
    • 1
    • 2
  1. 1.Department of Technology ManagementEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Dept. of Industrial EngineeringPohang University of Science and TechnologyNam-gu, PohangSouth Korea

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