A Frequency-Based Algorithm for Workflow Outlier Mining

  • Yu-Cheng Chuang
  • PingYu Hsu
  • MinTzu Wang
  • Sin-Cheng Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6485)


The concept of workflow is critical in the ERP (Enterprise Resources Planning) system. Any workflow that is irrationally and irregularly designed will not only lead to an ineffective operation of enterprise but also limit the implementation of an effective business strategy. The research proposes an algorithm which makes use of the workflow’s executed frequency, the concept of distance-based outlier detection, empirical rules and Method of Exhaustion to mine three types of workflow outliers, including less-occurring workflow outliers of each process (abnormal workflow of each process), less-occurring workflow outliers of all processes (abnormal workflow of all processes) and never-occurring workflow outliers (redundant workflow). In addition, this research adopts real data to evaluate workflow mining feasibility. In terms of the management, it will assist managers and consultants in (1) controlling exceptions in the process of enterprise auditing, and (2) simplifying the business process management by the integration of relevant processes.


ERP BPM Workflow mining Data mining Outlier detection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Process mining: discovering workflow models from event-based data. In: Kr€ose, B., de Rijke, M., Schreiber, G., van Someren, M. (eds.) Proceedings of the 13th Belgium–Netherlands Conference on Artificial Intelligence (BNAIC 2001), pp. 283–290 (2001)Google Scholar
  2. 2.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data. In: Hoste, V., de Pauw, G. (eds.) Proceedings of the 11th Dutch-Belgian Conference on Machine Learning (Benelearn 2001), pp. 93–100 (2001)Google Scholar
  3. 3.
    Arning, A., Agrawal, R., Raghavan, P.: A linear method for deviation detection in large databases. In: Proceedings of the KDD 1996, pp. 164–169 (1996)Google Scholar
  4. 4.
    Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the SIGMOD 2000, pp. 93–104 (2000)Google Scholar
  5. 5.
    Schimm, G.: Process Mining,
  6. 6.
  7. 7.
    Hawkins, D.: Identification of outliers. Chapman & Hall, Reading (1980)CrossRefzbMATHGoogle Scholar
  8. 8.
    Herbst, J.: A machine learning approach to workflow management. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 183–194. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Herbst, J., Karagiannis, D.: An inductive approach to the acquisition and adaptation of workflow models. In: Ibrahim, M., Drabble, B. (eds.) Proceedings of the IJCAI 1999 Workshop on Intelligent Workflow and Process Management: The New Frontier for AI in Business, Stockholm, Sweden, pp. 52–57 (August 1999)Google Scholar
  10. 10.
    Herbst, J., Karagiannis, D.: Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. In: Proceedings of the Ninth International Workshop on Database and Expert Systems Applications, pp. 745–752. IEEE, Los Alamitos (1998)Google Scholar
  11. 11.
    Herbst, J.: Dealing with concurrency in workflow induction. In: Baake, U., Zobel, R., Al-Akaidi, M. (eds.) European Concurrent Engineering Conference, SCS Europe (2000)Google Scholar
  12. 12.
    Herbst, J.: Ein induktiver Ansatz zur Akquisition und Adaption von Workflow-Modellen, Ph.D. thesis, Universit€at Ulm (November 2001)Google Scholar
  13. 13.
    Cook, J.E., Wolf, A.L.: Event-based detection of concurrency. In: Proceedings of the Sixth International Symposium on the Foundations of Software Engineering (FSE-6), pp. 35–45 (1998)Google Scholar
  14. 14.
    Cook, J.E., Wolf, A.L.: Software process validation: Quantitatively measuring the correspondence of a process to a model. ACM Transactions on Software Engineering and Methodology 8(2), 147–176 (1999)CrossRefGoogle Scholar
  15. 15.
    Knorr, E., Ng, R.: A unified notion of outliers: Properties and computation. In: Proceedings of the KDD 1997, pp. 219–222 (1997)Google Scholar
  16. 16.
    Knorr, E., Ng, R.: Algorithms for mining distance-based outliers in large datasets. In: Proceedings of the VLDB 1998, pp. 392–403 (1998)Google Scholar
  17. 17.
    Knorr, E., Ng, R.: Finding intentional knowledge of distance-based outliers. In: Proceedings of the VLDB 1999, pp. 211–222 (1999)Google Scholar
  18. 18.
    Jansen-Vullers, M.H., van der Aalst, W.M.P., Rosemann, M.: Mining configurable enterprise information systems. Data & Knowledge Engineering 56, 195–244 (2006)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Smith, H., Fingar, P.: Business Process Management: The Third Wave. Meghan-Kiffer Press, Tampa (2002)Google Scholar
  21. 21.
    Yamanishi, K., Takeuchi, J.: A unifying framework for detecting outliers and change points from non-stationary time series data. In: KDD 2002, pp. 676–681 (2002)Google Scholar
  22. 22.
    Yamanishi, K., Takeuchi, J.: Discovering outlier filtering rules from unlabeled data-combining a supervised learner with an unsupervised learner. In: Proceedings of the KDD 2001, pp. 389–394 (2001)Google Scholar
  23. 23.
    Yamanishi, K., Takeuchi, J., Williams, G., Milne, P.: On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms. Data Mining and Knowledge Discovery, 275–300 (2004)Google Scholar
  24. 24.
    He, Z., Xu, X., Huang, J.Z., Deng, S.: Mining class outliers: concepts, algorithms and applications in CRMGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yu-Cheng Chuang
    • 1
  • PingYu Hsu
    • 1
  • MinTzu Wang
    • 2
  • Sin-Cheng Chen
    • 1
  1. 1.Department of Business AdministrationNational Central UniversityTaiwan
  2. 2.Department of Information ManagementTechnology and Science Institute of Northern TaiwanTaiwan

Personalised recommendations