Process mining

Process mining is a process management technique that allows for the analysis of business processes based on event logs. Process mining uses specialized data-mining algorithms to extract knowledge from event logs recorded by an information system. Process mining aims at efficiency and understanding of processes by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs.[1] This is also known in the industry as Automated Business Process Discovery (ABPD)[2]

Overview

Process mining techniques are often used when no formal description of the process can be obtained by other approaches, or when the quality of an existing documentation is questionable. For example, the audit trails of a workflow management system, the transaction logs of an enterprise resource planning system, and the electronic patient records in a hospital can be used to discover models describing processes, organizations, and products.[3] Moreover, such event logs can also be used to compare event logs with some prior model to see whether the observed reality conforms to a prescriptive or descriptive model.

Contemporary management trends such as BAM (Business Activity Monitoring), BOM (Business Operations Management), BPI (business process intelligence) illustrate the interest in supporting the diagnosis functionality in the context of Business Process Management technology (e.g., Workflow Management Systems but also other process-aware information systems).

Application

Process mining follows the options known from business process engineering and goes beyond with feedback to business process modeling:[4]

Classification

There are three classes of process mining techniques. This classification is based on whether there is a prior model and, if so, how it is used.

See the book Process Mining: Discovery, Conformance and Enhancement of Business Processes by Wil van der Aalst for details.

Software for process mining

A software framework for the evaluation of process mining algorithms has been developed at the Eindhoven University of Technology by Wil van der Aalst and others, and is available as an open source toolkit.

Process Mining functionality is also offered by the following commercial vendors:

See also

References

  1. Process mining website. Accessed April 18th, 2011.
  2. Gartner Definition. Accessed January 6th, 2015.
  3. Kirchmer, M., Laengle, S., & Masias, V. (2013). Transparency-Driven Business Process Management in Healthcare Settings [Leading Edge]. Technology and Society Magazine, IEEE, 32(4), 14-16.
  4. Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer Verlag, Berlin (ISBN 978-3-642-19344-6).
  5. 1 2 Aalst, W. van der, Weijters, A., & Maruster, L. (2004). Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering, 16 (9), 1128–1142.
  6. Π-calculus
  7. Agrawal, R., Gunopulos, D., & Leymann, F. (1998). Mining Process Models from Workflow Logs. In Sixth international conference on extending database technology (pp. 469–483).
  8. Cook, J., & Wolf, A. (1998). Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology, 7 (3), 215–249.
  9. Datta, A. (1998). Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches. Information Systems Research, 9 (3), 275–301.
  10. Weijters, A., & Aalst, W. van der (2003). Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering, 10 (2), 151–162.
  11. Aalst, W. van der, Beer, H., & Dongen, B. van (2005). Process Mining and Verification of Properties: An Approach based on Temporal Logic. In R. Meersman & Z. T. et al. (Eds.), On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2005 (Vol. 3760, pp. 130–147). Springer-Verlag, Berlin.
  12. Rozinat, A., & Aalst, W. van der (2006a). Conformance Testing: Measuring the Fit and Appropriateness of Event Logs and Process Models. In C. Bussler et al. (Ed.), BPM 2005 Workshops (Workshop on Business Process Intelligence) (Vol. 3812, pp. 163–176). Springer-Verlag, Berlin.
  13. Process Mining
  14. Prom Framework
  15. Prom Import Framework
  16. Interstage Automated Process Discovery
  17. Disco
  18. Fluxicon
  19. QPR ProcessAnalyzer
  20. Perceptive Process Mining
  21. Celonis Process Mining
  22. SNP BPA
  23. minit
  24. http://www.cognizantzdlc.com
  25. "Business Process Management Tool | myInvenio". My-Invenio. Retrieved 2015-12-03.
  26. Lana Labs

Further reading

External links

This article is issued from Wikipedia - version of the Thursday, May 05, 2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.