Process mining

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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.


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).


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[further explanation needed] 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.


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][circular reference] 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[edit]

Several open source process mining toolkits are available:

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.
  • Fhinck [23] the Process Mining solution offered by the Google acelerated startup "Fhinck", an small app logged in to each machine understanding all digital steps of million users from the beginning till the end of his journey, creating insights to easy map RPA, People Analytics
  • Livejourney [24] a predictive Process Mining Software with a dynamic BPM integrated
  • BIP Process Mining Tool [25] the Process Mining solution offered by Business Integration Partners Spa
  • Celonis Process Mining,[26] the Process Mining solution offered by Celonis
  • Disco[27] is a complete Process Mining software by Fluxicon.[28]
  • EverFlow,[29] the Process Mining solution offered by Accelera Labs
  • Interstage Automated Process Discovery,[30] a Process Mining service offered by Fujitsu, Ltd. as part of the Interstage Integration Middleware Suite.
  • LANA Process Mining[31] is a process mining tool featuring discovery and conformance checking by Lana Labs.[32]
  • Minit[33] is a Process Mining software offered by Minit j.s.a.
  • MPM ProcessMining[34] is a Process Mining tool by Mehrwerk AG based on Qlik Platform [35].
  • myInvenio[36] cloud and on-premises solution by Cognitive Technology Ltd.
  • Logpickr Process Mining [37] the Process Mining solution combining Process Mining, Big Data and Artificial Intelligence algorithms, offered by Logpickr
  • PAFnow Process Mining [38] Process Mining Tool, Combining the Best of Business Intelligence, Process Intelligence and Artificial Intelligence in one solution.
  • Perceptive Process Mining,[39] the Process Mining solution by Perceptive Software (formerly Futura Reflect / Pallas Athena Reflect).
  • ProcessGold[40] Enterprise Platform, an integration of Process Mining & Business Intelligence.
  • QPR ProcessAnalyzer,[41] Process Mining software for Automated Business Process Discovery (ABPD).
  • Signavio Process Intelligence[42], Process Mining software by Signavio.
  • SNP Business Process Analysis,[43] the SAP-focused Process Mining solution by SNP Schneider-Neureither & Partner AG
  • StereoLOGIC [44] Process Analytics [45] - one of the oldest and leading offerings in the market, complementing it's offering with employee-based workplace mining [46]
  • TimelinePI - Intelligent Process Mining [47] Real Process Intelligence

See also[edit]


  1. ^ "Process Mining (Definition)". Process Mining Group, Eindhoven University of Technology. 24 Aug 2011. Retrieved 18 Apr 2011.
  2. ^ "Automated Business Process Discovery (ABPD)". Gartner, Inc. 2015. Retrieved 6 Jan 2015.Gartner Definition.
  3. ^ Dumas, Marlon; La Rosa, Marcello; Mendling, Jan; Reijers, Hajo A. (2018). Fundamentals of Business Process Management (2nd ed.). Springer.
  4. ^ 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.
  5. ^ Luis M. Camarinha-Matos, Frederick Benaben, Willy Picard (2015). Risks and Resilience of Collaborative Networks
  6. ^ Symeon Christodoulou, Raimar Scherer (2016). eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2016
  7. ^ Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer Verlag, Berlin (ISBN 978-3-642-19344-6).
  8. ^ a b 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.
  9. ^ Π-calculus
  10. ^ Agrawal, R., Gunopulos, D., & Leymann, F. (1998). Mining Process Models from Workflow Logs. In Sixth international conference on extending database technology (pp. 469–483).
  11. ^ 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.
  12. ^ Datta, A. (1998). Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches. Information Systems Research, 9 (3), 275–301.
  13. ^ 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.
  14. ^ 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.
  15. ^ 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.
  16. ^ Apromore
  17. ^ bupaR
  18. ^ [1]
  19. ^ PMLAB
  20. ^ ProM
  21. ^ Proceset
  22. ^ "Archived copy". Archived from the original on 2011-02-21. Retrieved 2011-01-11.CS1 maint: Archived copy as title (link)
  23. ^ Fhinck
  24. ^ Livejourney
  25. ^ [2]
  26. ^ Celonis Process Mining
  27. ^ Disco
  28. ^ Fluxicon
  29. ^ EverFlow
  30. ^ Interstage Automated Process Discovery
  31. ^ Lana Process Mining
  32. ^ Lana Labs
  33. ^ Minit
  34. ^ MPM-Mehrwerk Process Mining
  35. ^ Qlik Data Analytics
  36. ^ My Invenio
  37. ^ Logpickr
  38. ^ PAFnow Process Mining
  39. ^ Perceptive Process Mining
  40. ^ ProcessGold
  41. ^ QPR ProcessAnalyzer
  42. ^ Signavio Process Intelligence
  43. ^ SNP BPA
  44. ^ StereoLOGIC
  45. ^ Process Mining Manifesto
  46. ^ Market Guide for Process Mining
  47. ^ TimelinePI Intelligent Process Mining

Further reading[edit]

  • 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 [3]

External links[edit]