Process mining is a process management technique that allows for the analysis of business processes based on event logs. The basic idea is to extract knowledge from event logs recorded by an information system. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs.
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. Moreover, such event logs can also be used to compare event logs with some prior 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).
- process analysis filters, orders and compresses logfiles for further insight into the connex of process operations.
- process design may be supported by feedback from process monitoring, which means basically action or event logging
- process enactment uses results from process mining based on logging for triggering further process operation
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.
- Discovery: There is no a priori model, i.e., based on an event log some model is constructed a process model can be discovered based on low-level events. For example, using the alpha algorithm, which is a didactically driven approach, where the authors state the lack of analytic capability for large event data volumes with such simple method. There exist many techniques to automatically construct process models (e.g., in terms of a Petri net) based some event log. 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 analysis: 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 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.
- Extension: There is a prior model also. 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 prior process model dynamically annotated with performance data (e.g., bottlenecks are shown by coloring parts of the process model).
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:
- Interstage Automated Process Discovery, a Process Mining service offered by Fujitsu, Ltd. as part of the Interstage Integration Middleware Suite.
- Nitro is a tool by Fluxicon for easily converting CSV and XLS event logs for ProM.
- ARIS Process Performance Manager, a Process Mining and Process Intelligence Tool offered by Software AG as part of the Process Intelligence Solution.
- QPR ProcessAnalyzer, a tool for Automated Business Process Discovery, and QPR ProcessAnalysis, a service based on the aforementioned, offered by QPR Software Plc
- Disco is a complete process mining software by Fluxicon.
- Perceptive Process Mining, the Process Mining solution by Perceptive Software (formerly Futura Reflect / Pallas Athena Reflect).
- Celonis Process Mining, the Process Mining solution offered by Celonis
- SNP Business Process Analysis, the SAP-focused Process Mining solution by SNP Schneider-Neureither & Partner AG
- Business Process Discovery
- Business Process Management
- Scientific workflow system
- Sequence mining
- Data mining
- Intention mining
- Process mining website. Accessed April 18th, 2011.
- Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer Verlag, Berlin (ISBN 978-3-642-19344-6).
- 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.
- 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.
- 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.
- 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.
- Process Mining
- Prom Framework
- Prom Import Framework
- Interstage Automated Process Discovery
- QPR ProcessAnalyzer
- QPR ProcessAnalysis
- QPR Software Plc
- Perceptive Process Mining
- Celonis Process Mining
- SNP BPA
- Aalst, W. van der (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer Verlag, Berlin (ISBN 978-3-642-19344-6).
- 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.
- Jans, M., van der Werf, J.M., Lybaert, N., Vanhoof, K. (2011) A business process mining application for internal transaction fraud mitigation, Expert Systems with Applications, 38 (10), 13351–13359
- 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).
- 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 (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.
- Huser V, Starren JB, EHR Data Pre-processing Facilitating Process Mining: an Application to Chronic Kidney Disease. AMIA Annu Symp Proc 2009 link