Discovering expressive process models from noised log data

ABSTRACT

Process-oriented systems have been increasingly attracting data mining researchers, mainly due to the advantages that the application of inductive process mining techniques to log data could open to both the analysis of complex processes and the design of new process models.

However, the actual impact of process mining in the industry is endangered by some simplifying assumptions these techniques relies on. In fact, current approaches have still problems to mine models over languages that allow for complex constructs, e.g., duplicate tasks, hidden tasks, non-free-choice constructs, and/or when noise is admitted in the log.

In this paper, some advances to facing these problems are made, by proposing an algorithm which can deal with duplicate and hidden tasks, as well as with the presence of noise and non-free choice relationships among process activities. Importantly, due to the local nature of the search strategy exploited by the algorithm, the proposed approach seems suited to scale in real-world application scenarios.

REFERENCES

• 1: R. Agrawal, D. Gunopulos, and F. Leymann. Mining process models from workflow logs. In Proc. 6th Int. Conf. on Extending Database Technology (EDBT'98), pages 469--483, 1998.
• 2: R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. of the 20th Int'l Conference on Very Large Databases, pages 487--499, 1994.
• 3: J. E. Cook, Z. Du, C. Liu, and A. L. Wolf. Discovering models of behavior for concurrent workflows. Computers in Industry, 53(3):297--319, 2004.
• 4: J. E. Cook and A. L. Wolf. Automating process discovery through event-data analysis. In Proc. 17th Int. Conf. on Software Engineering (ICSE'95), pages 73--82, 1995.
• 5: J. E. Cook and A. L. Wolf. Event-based detection of concurrency. In Proc. 6th Int. Symposium on the Foundations of Software Engineering (FSE'98), pages 35--45, 1998.
• 6: J. E. Cook and A. L. Wolf. Software process validation: Quantitatively measuring the correspondence of a process to a model. ACM Trans. Softw. Eng. Methodol., 8(2):147--176, 1999.
• 7: M. Golani and S. S. Pinter. Generating a process model from a process audit log. In Intl. Conf. on Business Process Management (BPM'03), pages 136--151, 2003.
• 8: G. Greco, A. Guzzo, and L. Pontieri. Mining hierarchies of models: From abstract views to concrete specifications. In Proc. of Int. Conf. on Business Process Management, pages 32--47, 2005.
• 9: G. Greco, A. Guzzo, L. Pontieri, and D. Saccà. Discovering expressive process models by clustering log traces. IEEE Transactions on Knowledge and Data Engineering, 18(8):1010--1027, 2006.
• 10: J. Herbst. Dealing with concurrency in workflow induction. In Procs. European Concurrent Engineering Conference, pages 169--174, 2000.
• 11: J. Herbst and D. Karagiannis. Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. Journal of Intelligent Systems in Accounting, Finance and Management, 9:67--92, 2000.
• 12: J. Herbst and D. Karagiannis. Workflow mining with InWoLvE. Computers in Industry, 53(3):245--264, 2004.
• 13: S. Y. Hwang and W. S. Yang. On the discovery of process models from their instances. Decision Support Systems, 34(1):41--57, 2002.
• 14: S. Junginger, H. Kuhn, R. Strobl, and D. Karagiannis. Ein geschafts-prozessmanagement-werkzeug der nachsten generation - adonis: Konzeption und anwendungen. Wirtschaftsinformatik, 42(3):392--401, 2000.
• 15: G. Schimm. Mining most specific workflow models from event-based data. In Proc. of Int. Conf. on Business Process Management, pages 25--40, 2003.
• 16: W. M. P. van der Aalst, A. K. A. de Medeiros, and A. J. M. M. Weijters. Genetic process mining. In Proc. of 26th International Conference on Applications and Theory of Petri Nets (ICATPN'05), pages 48--69, 2005.
• 17: W. M. P. van der Aalst, A. K. A. de Medeiros, and A. J. M. M. Weijters. Genetic process mining: A basic approach and its challenges. In Workshop on Business Process Intelligence (BPI'05), 2005.
• 18: W. M. P. van der Aalst, L. Maruster, and T. Weijters. Workflow mining: Discovering process models from event logs. IEEE Transanctions on Knowledge Data Engineering, 16(9):1128--1142, 2004.
• 19: W. M. P. van der Aalst and B. F. van Dongen. Discovering workflow performance models from timed logs. In Proc. Int. Conf. on Engineering and Deployment of Cooperative Information Systems (EDCIS'02), pages 45--63, 2002.
• 20: W. M. P. van der Aalst, B. F. van Dongen, A. K. A. de Medeiros, H. M. W. Verbeek, and A. J. M. M. Weijters. The prom framework: A new era in process mining tool support. In Proc. of 26th International Conference on Applications and Theory of Petri Nets (ICATPN'05), pages 444--454, 2005.
• 21: W. M. P. van der Aalst, B. F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A. J. M. M. Weijters. Workflow mining: A survey of issues and approaches. Data and Knowledge Engineering, 47(2):237--267, 2003.
• 22: W. M. P. van der Aalst and K. M. van Hee. Workflow Management: Models, Methods, and Systems. MIT Press, 2002.
• 23: W. M. P. van der Aalst, A. J. M. M. Weijters, and L. Maruster. Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(9):1128--1142, 2004.
• 24: B. F. van Dongen and W. M. P. van der Aalst. Multi-phase process mining: Aggregating instance graphs into EPCs and Petri Nets. In Proc. Int. Work. on Applications of Petri Nets to Coordination, Worklflow and Business Process Management (PNCWB) at the ICATPN 2005, 2005.
• 25: A. J. M. M. Weijters and W. M. P. van der Aalst. Rediscovering workflow models from event-based data using little thumb. Integrated Computer-Aided Engineering, 10(2):151--162, 2003.
• 26: L. Wen, J. Wang, and J. Sun. Detecting implicit dependencies between tasks from event logs. In Proc. of 8th Asia-Pacific Web Conference, pages 591--603, 2006.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Keywords:
process mining, workflow management systems