Process mining

Process mining techniques allow for the analysis of business processes based on event logs. They are often used when no formal description of the process can be obtained by other means, 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. Moreover, such event logs can also be used to compare event logs with some "a priori" model to see whether the observed reality conforms to some 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).

Classification

There are three classes of process mining techniques. This classification is based on whether there is an "a priori" model and, if so, how it is used.
* Discovery: There is no "a priori" model, i.e., based on an event log some model is constructed. For example, using the alpha algorithm (Aalst et al., 2004) a process model can be discovered based on low-level events. There exist many techniques to automatically construct process models (e.g., in terms of a Petri net) based some event log (Aalst et al., 2004; Agrawal et al., 1998; Cook & Wolf, 1998; Datta, 1998; Weijters & Aalst, 2003). Recently, process mining research also started to target the other perspectives (e.g., data, resources, time, etc.). For example, the technique described in (Aalst, Reijers, & Song, 2005) can be used to construct a social network.
* Conformance: There is an "a priori" model. This model is compared with the event log and discrepancies between the log and the model are analyzed. For example, there may be a process model indicating that purchase orders of more than 1 million euro require two checks. Another example is the checking of the so-called “four-eyes” principle. Conformance checking may be used to detect deviations, to locate and explain these deviations, and to measure the severity of these deviations. An example is the conformance checker described in (Rozinat & Aalst, 2006a) which compares the event log with some "a priori" process model expressed in terms of a Petri net.
* Extension: There is an "a priori" model. This model is extended with a new aspect or perspective, i.e., the goal is not to check conformance but to enrich the model. An example is the extension of a process model with performance data, i.e., some "a priori" process model is used to project the bottlenecks on. Another example is the decision miner described in (Rozinat & Aalst, 2006b) which takes an "a priori" process model and analyzes every choice in the process model. For each choice the event log is consulted to see which information is typically available the moment the choice is made. Then classical data mining techniques are used to see which data elements influence the choice. As a result, a decision tree is generated for each choice in the process.

Software for process mining

A software framework for the evaluation of process mining algorithms has been developed at the Eindhoven University of Technology and is available as an open source toolkit.
* [http://www.processmining.org Process Mining]
* [http://prom.sourceforge.net Prom Framework]
* [http://promimport.sourceforge.net Prom Import Framework]

References

* 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.
* Aalst, W. van der, Dongen, B. van, Herbst, J., Maruster, L., Schimm, G., & Weijters, A. (2003). Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering, 47 (2), 237-267.
* Aalst, W. van der, Reijers, H., & Song, M. (2005). Discovering Social Networks from Event Logs. Computer Supported Cooperative work, 14 (6), 549-593.
* 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.
* Agrawal, R., Gunopulos, D., & Leymann, F. (1998). Mining Process Models from Workflow Logs. In Sixth international conference on extending database technology (pp. 469-483).
* 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.
* Datta, A. (1998). Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches. Information Systems Research, 9 (3), 275-301.
* Dongen, B. van, Medeiros, A., Verbeek, H., Weijters, A., & Aalst, W. van der (2005). The ProM framework: A New Era in Process Mining Tool Support. In G. Ciardo & P. Darondeau (Eds.), Application and Theory of Petri Nets 2005 (Vol. 3536, pp. 444-454). Springer-Verlag, Berlin.
* Dumas, M., Aalst, W. van der, & Hofstede, A. ter (2005). Process-Aware Information Systems: Bridging People and Software through Process Technology. Wiley & Sons.
* Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., & Shan, M. (2004). Business Process Intelligence. Computers in Industry, 53 (3), 321-343.
* Grigori, D., Casati, F., Dayal, U., & Shan, M. (2001). Improving Business Process Quality through Exception Understanding, Prediction, and Prevention. In P. Apers, P. Atzeni, S. Ceri, S. Paraboschi, K. Ramamohanarao, & R. Snodgrass (Eds.), Proceedings of 27th international conference on Very Large Data Bases (VLDB’01) (pp. 159-168). Morgan Kaufmann.
* IDS Scheer. (2002). ARIS Process Performance Manager (ARIS PPM): Measure, Analyze and Optimize Your Business Process Performance (whitepaper). (http://www.ids-scheer.com IDS Scheer, Saarbruecken, Gemany)
* Ingvaldsen, J.E., & J.A. Gulla. (2006). Model Based Business Process Mining. Journal of Information Systems Management, Vol. 23, No. 1, Special Issue on Business Intelligence, Auerbach Publications
* zur Muehlen, M. (2004). Workflow-based Process Controlling: Foundation, Design and Application of workflow-driven Process Information Systems. Logos, Berlin.
* zur Muehlen, M., & Rosemann, M. (2000). Workflow-based Process Monitoring and Controlling - Technical and Organizational Issues. In R. Sprague (Ed.), Proceedings of the 33rd Hawaii international conference on system science (HICSS-33) (pp. 1-10). IEEE Computer Society Press, Los Alamitos, California.
* 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.
* Rozinat, A., & Aalst, W. van der (2006b). Decision Mining in ProM. In S. Dustdar, J. Faideiro, & A. Sheth (Eds.), International Conference on Business Process Management (BPM 2006) (Vol. 4102, pp. 420-425). Springer-Verlag, Berlin.
* Sayal, M., Casati, F., Dayal, U., & Shan, M. (2002). Business Process Cockpit. In Proceedings of 28th international conference on very large data bases (VLDB’02) (pp. 880-883). Morgan Kaufmann.
* 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.

Related Links

* Workflow
* Business Process Management
* Data mining