Process Mining


Today’s organizations use a wide range of information systems to support their business processes. Such support systems often record and log an abundance of data, containing a variety of events that can be linked back to the occurrence of a task in an originating business process. Process mining starts from these event logs as the cornerstone of analysis and aims to derive knowledge to model, improve and extend operational processes “as they happen” in the organization.

The topic of process mining can be situated at the intersection of the fields of Business Process Management (BPM) and data mining. It is inherently related to data mining and to the more general domain of knowledge discovery in databases (KDD) since the nature of its objectives is extracting useful information from large data repositories. Likewise, process mining is strongly associated with BPM because of its purpose of gaining insight into business processes. As a result, process mining fits flawlessly into the BPM life cycle framework.

The different activities of analysis which are made possible by process mining can be distinguished on the basis of two dimensions. The first dimension refers to four different types of information perspectives from which an event log can be looked at:

  • Control-flow perspective: focuses on the ordering of activities in the event log.
  • Organizational/resource perspective: the people, employees, departments who perform the activities.
  • Case data perspective: extra data elements that might be available in the event log.
  • Exception perspective: problems and failures (e.g.: the abortion of certain activities).

The second dimension partitions the different available process mining techniques and activities in three groups:

  • Discovery: searches for underlying models and knowledge. For example: the discovery of an “as-is” process model from event log data.
  • Conformance and compliance: evaluates event logs in comparison with an existing process model to analyse where deviation occur, or checks recorded data against documented models, policies and legislation in order to detect anomalies and monitor process execution.
  • Enhancement: enriches business processes with additional information. For example by adding organizational data to a control-flow based process model to inspect how people work together.

Performing process mining over these orthogonal dimensions allows the organization to answer questions such as “What is the actual, real process?”, “Are our policies applied in day-to-day business practices?” and “Can we predict future behaviour of not-yet-completed process instances?”

Process discovery is one of the main process mining tasks and aims to induce a process model from real life, recorded event log data.

A good understanding of processes is vital for fulfilling business requirements such as verifying and guaranteeing business process compliance, setting up a coherent access control policy and optimizing and redesigning business processes. A better understanding will eventually enable organizations to provide better, automated support for their business processes in flexible, process-aware information systems. Traditionally, practitioners have been obtaining insight into processes using interviewing techniques. However, factors like extensive business process automation, strategic behaviour with interviewees and socio-economic factors such as personnel attrition, mergers and acquisitions have often shown these inherently qualitative interviewing techniques to be less suitable. In many organizations, recorded event logs are already available and conceal a presently-untapped reservoir of knowledge about the way employees and customers conduct every-day business transactions. Popular Enterprise Resource Planning (ERP) systems such as SAP R/3, Oracle e-Business Suite and workflow management systems (WfMS) such as ARIS, TIBCO and Microsoft Biztalk already keep track of these event logs.

The research team of professor Baesens currently studies the following topics in this context:

  • New process quality evaluation metrics for robust conformance checking.
  • Investigating the application of process mining related techniques to marketing analytics and customer-centric scenarios such as customer journey mapping.
  • Process clustering techniques which help to “untangle” complex models.
  • The use of artificially generated negative events for the discovery of process models from event logs.
  • Assessment of currently available process discovery evaluation metrics via the development of strong benchmarking tools.
  • Application of machine learning techniques and probabilistic learning approaches to the analysis of event logs.
  • Industry applications of process mining: telecom industry, banking industry, government.

Notable Publications

  • vanden Broucke, S., De Weerdt, J., Vanthienen, J., Baesens, B. (2014). Determining process model precision and generalization with weighted artificial negative events. IEEE Transactions on Knowledge and Data Engineering, 26 (8), 1877-1889.
  • Caron, F., vanden Broucke, S., Vanthienen, J., Baesens, B. (2014). Advanced rule-based process analytics: applications for risk response decisions and management control activities. Expert Systems with Applications, accepted.
  • Seret, A., vanden Broucke, S., Baesens, B., Vanthienen, J. (2014). A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Systems with Applications, 41 (10), 4648-4657.
  • De Weerdt, J., Schupp, A., Vanderloock, A., Baesens, B. (2013). Process mining for the multi-faceted analysis of business processes – A case study in a financial services organization. Computers in Industry64, 57-67.
  • De Weerdt, J., vanden Broucke, S. (2014). SECPI: searching for explanations for clustered process instances. Proceeding of the 12th International Conference on Business Process Management, BPM 2014: Vol. accepted. International Conference (BPM 2014). Haifa (Israel), 7-11 September 2014.
  • De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B. (2012). A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Information Systems37 (7), 654-676.
  • vanden Broucke, S., Munoz-Gama, J., Carmona, J., Baesens, B., Vanthienen, J. (2014). Event-based real-time decomposed conformance analysis. On the Move Federated Conferences & Workshops: Vol. accepted. International Conference on Cooperative Information Systems (CoopIS 2014). Amantea, Calabria (Italy), 27-31 October 2014 Springer.
  • vanden Broucke, S., Vanthienen, J., Baesens, B. (2014). Declarative process discovery with evolutionary computing. 2014 IEEE Congress on Evolutionary Computation Proceedings. 2014 IEEE. Beijing (China), 6-11 July 2014 (pp. 2412-2419) IEEE.
  • De Weerdt, J., vanden Broucke, S., Vanthienen, J., Baesens, B. (2012). Active trace clustering for improved process discovery. IEEE Transactions on Knowledge and Data Engineeringaccepted.
  • Goedertier, S., De Weerdt, J., Martens, D., Vanthienen, J., Baesens, B. (2011). Process discovery in event logs: an application in the telecom industry. Applied Soft Computing, 11 (2), 1697-1710.
  • Goedertier, S., Martens, D., Vanthienen, J., Baesens, B. (2009). Robust process discovery with artificial negative events. Journal of Machine Learning Research, 10, 1305-1340.
  • vanden Broucke, S., De Weerdt, J., Vanthienen, J., Baesens, B. (2013). A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, part of the IEEE Symposium Series on Computational Intelligence 2013, SSCI 2013: vol. accepted. IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2013). Singapore, 16-19 April 2013.
  • Caron, F., vanden Broucke, S., Vanthienen, J., Baesens, B. (2012). On the distinction between truthful, invisible, false and unobserved eventsProceedings of the 18th Americas Conference on Information Systems: vol. accepted. Americas Conference on Information Systems. Seattle, Washington (US), 9-12 August 2012.
  • De Weerdt, J., vanden Broucke, S., Vanthienen, J., Baesens, B. (2012). Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. Evolutionary Computation (CEC), 2012 IEEE Congress on. Congress on Evolutionary Computation (CEC), 2012 IEEE. Brisbane (Australia), 10-15 June 2012 (pp. 1-8).
  • vanden Broucke, S., De Weerdt, J., Baesens, B., Vanthienen, J. (2012). An improved artificial negative event generator to enhance process event logs. Lecture Notes in Computer Science: vol. Accepted. International Conference on Advanced Information Systems Engineering (CAiSE’12). Gdansk (Poland), 25-29 June 2012.
  • De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B. (2011). A robust F-measure for evaluating discovered process models. CIDM. IEEE Symposium Series in Computational Intelligence 2011 (SSCI 2011). Paris (France), 11-15 April 2011 (pp. 148-155) IEEE.
  • Caron, F., Vanthienen, J., De Weerdt, J., Baesens, B. (2011). Advanced care-flow mining and analysis. In Daniel, F. (Ed.), Barkaoui, K. (Ed.), Dustdar, S. (Ed.), Lecture Notes in Business Information Processing: Vol. 99. Business Process Management Workshops (BPM 2011). Clermont-Ferrand (France), 28 August-2 September 2011 (pp. 167-168).
  • De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B. (2010). A critical evaluation study of model-log metrics in process discovery. In zur Muehlen, M. (Ed.), Su, J. (Ed.), Business Process Management Workshops: Vol. 66. Workshop on Business Process Intelligence (BPI2010). New Jersey (US), 14-16 September 2010 (pp. 158-169) Springer.
  •  Goedertier, S., Martens, D., Baesens, B., Haesen, R., Vanthienen, J. (2008). Process Mining as First-Order Classification Learning on Logs with Negative Events. Lecture Notes in Computer Science: Vol. 4928. Workshop on Business Process Intelligence (BPI 07) at BPM 2007. Brisbane, Australia, 24 September 2007 (pp. 42-53) Springer.