1. Overview

2. Application

3. Classification

4. Software for process mining

5. See also

6. References

7. Further reading

8. External links

Process mining is a family of techniques in the field of process management that support the analysis of business processes based on event logs. During process mining, specialized data mining algorithms are applied to event log data 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] However, in academic literature^[3] the term Automated Business Process Discovery is used in a narrower sense to refer specifically to techniques that take as input an event log and produce as output a business process model. The term Process Mining is used in a broader setting to refer not only to techniques for discovering process models, but also techniques for business process conformance and performance analysis based on event logs.

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.^[4] 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. It is required that the event logs data be linked to a case ID, activities, and timestamps^[5]{{,}}^[6].

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:^[7]

• process analysis filters, orders and compresses logfiles for further insight into the connex{{elucidate|date=September 2016}} 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

A database of applications of Process Mining list all the major process mining initiatives.

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).^[8] Many established techniques exist for automatically constructing process models (for example, Petri net, pi-calculus^[9]{{Better source|reason=per WP:CIRCULAR|date=August 2015}} expression) based on an event log.^{[8][10][11][12][13]} 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),^[14] 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)^[15] 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.
• Performance Mining: Used when there is an a priori model. The model is extended with a new performance information such as processing times, cycle times, waiting times, costs, etc., so that the goal is not to check conformance, but rather to improve the performance of the existing model with respect to certain process performance measures. 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

Several open source process mining toolkits are available:

• Apromore^[16]
• bupaR^[17]
• PM4Py^[18]
• PMLAB^[19]
• ProM,^[20]

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

• Proceset ^[21] the Process Mining solution offered by Infomaximum
• ARIS Process Mining also known as PPM - Process Performance Manager,^[22] a Process Mining and Process Intelligence Tool offered by Software AG as part of the Digital Business Platform Solution.
• Livejourney ^[23] a predictive Process Mining Software with a dynamic BPM integrated
• BIP Process Mining Tool ^[24] the Process Mining solution offered by Business Integration Partners Spa
• Celonis Process Mining,^[25] the Process Mining solution offered by Celonis
• Disco^[26] is a complete Process Mining software by Fluxicon.^[27]
• EverFlow,^[28] the Process Mining solution offered by Accelera Labs
• Interstage Automated Process Discovery,^[29] a Process Mining service offered by Fujitsu, Ltd. as part of the Interstage Integration Middleware Suite.
• LANA Process Mining^[30] is a process mining tool featuring discovery and conformance checking by Lana Labs.^[31]
• Minit^[32] is a Process Mining software offered by Minit j.s.a.
• MPM ProcessMining^[33] is a Process Mining tool by Mehrwerk AG based on Qlik Platform ^[34].
• myInvenio^[35] cloud and on-premises solution by Cognitive Technology Ltd.
• Logpickr Process Mining ^[36] the Process Mining solution combining Process Mining, Big Data and Artificial Intelligence algorithms, offered by Logpickr
• PAFnow Process Mining ^[37] Process Mining Tool, Combining the Best of Business Intelligence, Process Intelligence and Artificial Intelligence in one solution.
• Perceptive Process Mining,^[38] the Process Mining solution by Perceptive Software (formerly Futura Reflect / Pallas Athena Reflect).
• ProcessGold^[39] Enterprise Platform, an integration of Process Mining & Business Intelligence.
• QPR ProcessAnalyzer,^[40] Process Mining software for Automated Business Process Discovery (ABPD).
• Signavio Process Intelligence^[41], Process Mining software by Signavio.
• SNP Business Process Analysis,^[42] the SAP-focused Process Mining solution by SNP Schneider-Neureither & Partner AG
• StereoLOGIC ^[43] Process Analytics ^[44] - one of the oldest and leading offerings in the market, complementing it's offering with employee-based workplace mining ^[45]
• TimelinePI - Intelligent Process Mining ^[46] Real Process Intelligence

See also

• Business Process Discovery
• Business Process Management
• Workflow
• Scientific workflow system
• Sequence mining
• Data mining
• Intention mining
• Data visualization

References

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 [https://independent.academia.edu/VojtechHuser/Papers/990979/EHR_Data_Pre-processing_Facilitating_Process_Mining_an_Application_to_Chronic_Kidney_Disease 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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