Process mining
Process mining is a process management technique that allows for the analysis of business processes based on event logs. During process mining, specialized data-mining algorithms are applied to event log datasets in order to identify trends, patterns and details contained in event logs recorded by an information system. Process mining aims to improve process efficiency and understanding of processes.[1] Process mining is also known 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 existing documentation is questionable. For example, application of process mining methodology to the audit trails of a workflow management system, the transaction logs of an enterprise resource planning system, or the electronic patient records in a hospital can result in models describing processes, organizations, and products.[3] Event log analysis can also be used to compare event logs with prior model(s) to understand whether the observations conform to a prescriptive or descriptive model.
Contemporary management trends such as BAM (Business Activity Monitoring), BOM (Business Operations Management), and BPI (business process intelligence) illustrate the interest in supporting diagnosis functionality in the context of Business Process Management technology (e.g., Workflow Management Systems and other process-aware information systems).
Application
Process mining follows the options established in business process engineering, then goes beyond those options by providing feedback for business process modeling:[4]
- 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 (action or event recording or logging)
- process enactment uses results from process mining based on logging for triggering further process operations
Classification
There are three classes of process mining techniques. This classification is based on whether there is a prior model and, if so, how the prior model is used during process mining.
- Discovery: Previous (a priori) models do not exist. Based on an event log, a new model is constructed or discovered based on low-level events. For example, using the alpha algorithm (a didactically driven approach).[5] Many established techniques exist for automatically constructing process models (for example, Petri net, pi-calculus[6] expression) based on an event log.[5][7][8][9][10] Recently, process mining research has started targeting the other perspectives (e.g., data, resources, time, etc.). One example is the technique described in (Aalst, Reijers, & Song, 2005),[11] which can be used to construct a social network.
- Conformance checking: Used when there is an a priori model. The existing model is compared with the process event log; 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 potential bottlenecks. Another example is the decision miner described in (Rozinat & Aalst, 2006b)[12] 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: Used when there is an a priori model. The model is extended with a new aspect or perspective, so that the goal is not to check conformance, but rather to improve the existing model. An example is the extension of a process model with performance data, i.e., some prior process model dynamically annotated with performance data.
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,[15] a Process Mining service offered by Fujitsu, Ltd. as part of the Interstage Integration Middleware Suite.
- Disco[16] is a complete Process Mining software by Fluxicon.[17]
- ARIS Process Performance Manager,[18] a Process Mining and Process Intelligence Tool offered by Software AG as part of the Process Intelligence Solution.
- QPR ProcessAnalyzer,[19] Process Mining software for Automated Business Process Discovery (ABPD).
- Perceptive Process Mining,[20] the Process Mining solution by Perceptive Software (formerly Futura Reflect / Pallas Athena Reflect).
- Celonis Process Mining,[21] the Process Mining solution offered by Celonis
- SNP Business Process Analysis,[22] the SAP-focused Process Mining solution by SNP Schneider-Neureither & Partner AG
- minit[23] is a Process Mining software offered by Gradient ECM
- myInvenio[24] cloud and on-premises solution by Cognitive Technology Ltd.
- LANA[25] is a process mining tool featuring discovery and conformance checking.
- ProcessGold[26] Enterprise Platform, an integration of Process Mining & Business Intelligence.
See also
- Business Process Discovery
- Business Process Management
- Workflow
- Scientific workflow system
- Sequence mining
- Data mining
- Intention mining
- Data visualization
References
- 1 2 "Process Mining (Definition)". processmining.org. Process Mining Group, Eindhoven University of Technology. 24 Aug 2011. Retrieved 18 Apr 2011.
- ↑ "Automated Business Process Discovery (ABPD)". Gartner.com. Gartner, Inc. 2015. Retrieved 6 Jan 2015.Gartner Definition.
- ↑ 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.
- ↑ Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer Verlag, Berlin (ISBN 978-3-642-19344-6).
- 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.
- ↑ Π-calculus
- ↑ 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.
- ↑ Prom Framework
- ↑ Prom Import Framework
- ↑ Interstage Automated Process Discovery
- ↑ Disco
- ↑ Fluxicon
- ↑
- ↑ QPR ProcessAnalyzer
- ↑ Perceptive Process Mining
- ↑ Celonis Process Mining
- ↑ SNP BPA
- ↑ minit
- ↑ My Invenio
- ↑ Lana Labs
- ↑ ProcessGold
Further reading
- 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
- 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.
- 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
- Ross-Talbot S, The importance and potential of descriptions to our industry. Keynote at The 10th International Federated Conference on Distributed Computing Techniques
External links
- Process mining research at Eindhoven University of Technology, the Netherlands.
- Process mining research at Ghent University, Belgium.
- Process mining research at University of Padua, Italy.
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