Computer Supported Cooperative Work (CSCW)

, Volume 14, Issue 6, pp 549–593 | Cite as

Discovering Social Networks from Event Logs

  • Wil M. P. van der Aalst
  • Hajo A. Reijers
  • Minseok Song
Article

Abstract

Process mining techniques allow for the discovery of knowledge based on so-called “event logs”, i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log within the setting of a large Dutch organization.

Keywords

business process management data mining Petri nets process mining social network analysis workflow management 

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References

  1. Agrawal, R., D. Gunopulos and F. Leymann (1998): Mining Process Models from Workflow Logs. Sixth International Conference on Extending Database Technology, pp. 469–483.Google Scholar
  2. Bavelas A. (1948) A Mathematical Model for Group Structures. Human Organization 7:16–30Google Scholar
  3. Begole, J., Tang J., Smith R. and Yankelovich N. (2002): Work Rhythms: Analyzing Visualizations of Awareness Histories of Distributed Groups. In: Neuwirth C., Rodden T. (eds) Proceedings of the 2002 ACM conference on Computer Supported Cooperative Work. ACM Press, New York, NY, USA, pp. 334–343CrossRefGoogle Scholar
  4. Bernard H., Killworth P., McCarty C., Shelley G. and Robinson S. (1990): Comparing Four Different Methods for Measuring Personal Social Networks. Social Networks 12:179–216CrossRefGoogle Scholar
  5. Bonacich P. (1987): Power and Centrality: A family of Measures. American Journal of Sociology 92: 1170–1182CrossRefGoogle Scholar
  6. Burt R. and Minor M. (1983) Applied Network Analysis: A Methodological Introduction. Sage, Newbury Park CA: Sage http://www.cbpweb.nl/index.htm.Google Scholar
  7. Clausen, S.E. (1998): Applied Correspondence Analysis: An Introduction, Sage Publications.Google Scholar
  8. CBP n.d., College Bescherming Persoonsgegevens (CBP; Dutch Data Protection Authority)Google Scholar
  9. Cook J. and Wolf A. (1998): Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology 7(3): 215–249CrossRefGoogle Scholar
  10. Culotta, A., R. Bekkerman and A. McCallum (2004): Extracting Social Networks and Contact Information from Email and the Web. Proceedings of the First Conference on Email and Anti-Spam (CEAS).Google Scholar
  11. Ellis C. (2000): An Evaluation Framework for Collaborative Systems. Technical Report, CU-CS-901-00, University of Colorado, Department of Computer Science, Boulder, USAGoogle Scholar
  12. Ellis C., Gibbs S. and Rein G. (1991) Groupware: Some issues and experiences. Communications of the ACM 34(1): 38–58CrossRefGoogle Scholar
  13. Farnham, S., S. Kelly, W. Portnoy and J. Schwartz (2004a): Wallop: Designing Social Software for Co-Located Social Networks. Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS’04). CA: IEEE Computer Society Press, Los AlamitosGoogle Scholar
  14. Farnham, S., W. Portnoy and A. Turski (2004b): Using Email Mailing Lists to Approximate and Explore Corporate Social Networks, In D. McDonald, S. Farnham and D. Fisher (eds.): Proceedings of the CSCW’04 Workshop on Social Networks.Google Scholar
  15. Feldman M. (1987): Electronic Mail and Weak Ties in Organizations. Office: Technology and People 3: 83–101CrossRefGoogle Scholar
  16. Fischer L. (eds) (2001): Workflow Handbook 2001, Workflow Management Coalition. Future Strategies, Lighthouse Point, FloridaGoogle Scholar
  17. Fisher D. and Dourish P. (2004): Social and Temporal Structures in Everyday Collaboration. In: Dykstra-Erickson E., Tscheligi M. (eds) Proceedings of the 2004 Conference on Human Factors in Computing Systems (CHI2004). ACM Press, New York, NY, USA, pp. 551–558CrossRefGoogle Scholar
  18. Freeman L. (1977): A Set of Measures of Centrality Based on Betweenness. Sociometry 40: 35–41CrossRefGoogle Scholar
  19. Freeman L. (1979) Centrality in Social Networks: Conceptual Clarification. Social Networks 1: 215–239CrossRefGoogle Scholar
  20. Gauch, H.G. (1982): Data Analysis in Community and Landscape Ecology, Cambridge University Press.Google Scholar
  21. Grigori, D., F. Casati, U. Dayal and M. Shan (2001): Improving Business Process Quality through Exception Understanding, Prediction, and Prevention. In P. Apers, P. Atzeni, S. Ceri, S. Paraboschi, K. Ramamohanarao and R. Snodgrass (eds.): Proceedings of 27th International Conference on Very Large Data Bases (VLDB’01), Morgan Kaufmann, pp. 159–168.Google Scholar
  22. Herbst, J. (2000): A Machine Learning Approach to Workflow Management. Proceedings 11th European Conference on Machine Learning, Vol. 1810 of Lecture Notes in Computer Science. Berlin: Springer-Verlag, pp. 183–194Google Scholar
  23. Herbst, J. (2001): Ein induktiver Ansatz zur Akquisition und Adaption von Workflow-Modellen, PhD thesis, Universität Ulm.Google Scholar
  24. Hulsman B. and Ippel P. (1994): Personeelsinformatiesystemen: De Wet Persoonsregistraties toegepast. Registratiekamer, The HagueGoogle Scholar
  25. IDS Scheer (2002): ARIS Process Performance Manager (ARIS PPM): Measure, Analyze and Optimize Your Business Process Performance (whitepaper). IDS Scheer, Saarbruecken, Gemany, http://www.ids-scheer.com.
  26. Jablonski S. and Bussler C. (1996) Workflow Management: Modeling Concepts, Architecture, and Implementation. International Thomson Computer Press, London, UKGoogle Scholar
  27. Leymann F. and Roller D. (1999) Production Workflow: Concepts and Techniques. Prentice-Hall PTR, Upper Saddle River, New Jersey, USAGoogle Scholar
  28. Malone T. (1995) Commentary on Suchman article and Winograd response. Computer Supported Cooperative Work 3(1): 37–38CrossRefGoogle Scholar
  29. Manna Z. and Pnueli A. (1991) The Temporal Logic of Reactive and Concurrent Systems: Specification. Springer-Verlag, New YorkGoogle Scholar
  30. Mitchell J. (1969): The Concept and Use of Social Networks. In: J. Mitchell (eds) Social Networks in Urban Situations. Manchester University Press, Manchester, pp. 1–50Google Scholar
  31. Moreno J. (1934) Who Shall Survive?. Nervous and Mental Disease Publishing Company, Washington, DCGoogle Scholar
  32. Mühlen M. and Rosemann M. (2000) Workflow-based Process Monitoring and Controlling - Technical and Organizational Issues. In: Sprague R.(eds) Proceedings of the 33rd Hawaii International Conference on System Science (HICSS-33). IEEE Computer Society Press, Los Alamitos, California, pp. 1–10Google Scholar
  33. Nardi B., Whittaker S., Isaacs E., Creech M., Johnson J. and Hainsworth J. (2002): Integrating Communication and Information Through ContactMap. Communications of the ACM 45(2):89–95CrossRefGoogle Scholar
  34. Nemati H. and Barko C. (2003) Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance. Idea Group Publishing, Hershey, PA, USAGoogle Scholar
  35. Ogata H., Yano Y., Furugori N. and Jin Q. (2001): Computer Supported Social Networking For Augmenting Cooperation. Computer Supported Cooperative Work 10(2): 189–209CrossRefGoogle Scholar
  36. Sauerwein L. and Linnemann J. (2001): Guidelines for Personal Data Processors: Personal Data Protection Act. Ministry of Justice, The HagueGoogle Scholar
  37. Sayal, M., F. Casati, U. Dayal and M. Shan (2002): Business Process Cockpit. Proceedings of 28th International Conference on Very Large Data Bases (VLDB’02). Morgan Kaufmann, pp. 880–883.Google Scholar
  38. Schimm G. (2000) Generic Linear Business Process Modeling. In: S. Liddle, Mayr H. and Thalheim B. (eds) Proceedings of the ER 2000 Workshop on Conceptual Approaches for E-Business and The World Wide Web and Conceptual Modeling Vol. 1921 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 31–39Google Scholar
  39. Scott J. (1992) Social Network Analysis. Sage, Newbury Park CA.Google Scholar
  40. Smith, M. (1999): Invisible Crowds in Cyberspace: Measuring and Mapping the Social Structure of Usenet. In M. Smith and P. Kollock (eds.): Communities in Cyberspace: Perspectives on New Forms of Social Organization, Routledge Press.Google Scholar
  41. Staffware (2002): Staffware Process Monitor (SPM), http://www.staffware.com.
  42. Suchman L. (1994): Do Categories Have Politics? The Language /Action Perspective Reconsidered. Computer Supported Cooperative Work 2(3): 177–190CrossRefGoogle Scholar
  43. Aalst W. and Dongen B. (2002) Discovering Workflow Performance Models from Timed Logs. In: Han Y., Tai S. and Wikarski D. (eds) International Conference on Engineering and Deployment of Cooperative Information Systems (EDCIS 2002) Vol. 2480 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 45–63Google Scholar
  44. Aalst W. and Hee K. (2002) Workflow Management: Models, Methods, and Systems. MIT press, Cambridge, MA.Google Scholar
  45. Aalst W. and Song M. (2004) Mining Social Networks: Uncovering Interaction Patterns in Business Processes. In: Desel J., Pernici B., Weske M. (eds) International Conference on Business Process Management (BPM 2004), Vol. 3080 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 244–260Google Scholar
  46. Aalst W. and Weijters A. (eds) (2004): Process Mining. Special Issue of Computers in Industry, Volume 53, Number 3. Elsevier Science Publishers, AmsterdamGoogle Scholar
  47. Aalst W., van Dongen B., Herbst J., Maruster L., Schimm G. and Weijters A. (2003) Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering 47(2): 237–267CrossRefGoogle Scholar
  48. Aalst W., Weijters A. and Maruster L. (2004): Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9):1128–1142CrossRefGoogle Scholar
  49. Wasserman S. and Faust K. (1994) Social Network Analysis: Methods and Applications. Cambridge University Press, CambridgeGoogle Scholar
  50. Weijters A. and Aalst W. (2003): Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering 10(2): 151–162Google Scholar
  51. Winograd T. (1994): Categories, Disciplines, and Social Coordination. Computer Supported Cooperative Work 2(3): 191–197CrossRefGoogle Scholar
  52. Winograd T. and Flores F. (1986) Understanding Computers and Cognition: A New Foundation for Design. Ablex, NorwoodGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Wil M. P. van der Aalst
    • 1
  • Hajo A. Reijers
    • 1
  • Minseok Song
    • 1
    • 2
  1. 1.Department of Technology ManagementEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Department of Industrial EngineeringPohang University of Science and TechnologyNam-guSouth Korea

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