Desire Lines in Big Data


The DELIBIDA (Desire Lines in Big Data) research program aims at developing new process mining techniques that are able to deal with huge event logs recorded for processes executed in possibly highly variable and heterogeneous contexts.

The goal of process mining is to extract process-related information from event logs, e.g., to automatically discover a process model. Despite recent advances in process mining there are important challenges that need to be addressed. In fact, the discovery of process models from event logs is notoriously difficult and major breakthroughs are needed for the large-scale application of process mining. DELIBIDA is composed of three research tracks aiming at such breakthroughs:

- In Track T1 we will develop techniques to decompose process mining problems (e.g., process discovery and conformance checking) into smaller problems that can be solved more efficiently and that can be distributed over a network of computers.

- Track T2 goes one step further. To support applications where it is impossible to store events over an extended period, on-the-fly process mining techniques will be developed that can learn (or check) process models without storing excessive amounts of events.

- Existing techniques require the analyst to restrict the scope to a single process model describing the behavior of a homogeneous group of cases in steady-state. In Track T3 we will develop comparative process mining techniques that systematically highlight commonalities and differences. This way we can deal with heterogeneous processes that are changing over time and that have many variants.


Wetenschappelijk artikel

  • H.M.W. Verbeek, W.M.P. van der Aalst(2014): Process Discovery and Conformance Checking Using Passages Fundamentals of Information pp. 103 - 8
  • W.M.P. van der Aalst, E. Damiani(2015): Processes Meet Big Data: Connecting Data Science with Process Science IEEE Transactions on Services Computing pp. 810 - 819
  • T.E. Ward, L. Cheng, S. Kotoulas, G. Theodoropoulos(2017): Improving the robustness and performance of parallel joins over distributed systems Journal of Parallel and Distributed Computing pp. 310 - 323






Prof. dr. ir. W.M.P. van der Aalst

Verbonden aan

Technische Universiteit Eindhoven, Faculteit Industrial Engineering & Innovation Sciences, Information Systems (IS)


Dr. L. Cheng, Long Cheng, S. Shabaninejad, A. Syamsiyah MSc, S.J. van Zelst


15/02/2014 tot 28/02/2018