Below, you find an overview of projects carried out in the group in which I am directly involved. For an overview of all projects of all group members, click here.
Desire Lines in Big Data
Despite recent advances in process mining there are still important challenges that need to be addressed. In particular with respect to handling large-scale event logs. DeLiBiDa aims to develop new techniques to deal with massive event data. There are various settings where it is impossible to store events over an extended period. Therefore, we want to develop techniques for storing large event logs efficiently, for example in databases. Furthermore, we aim to develop in-database (pre)processing techniques to facilitate existing as well as new to be developed process mining technology. Finally, we plan to develop query techniques to make event-data quickly accessible for processing.
Staff involved
- Boudewijn van Dongen (Associate professor)
- Long Cheng (postDoc)
- Alifah Syamsiyah (PhD Candidate)
- Bas van Zelst (PhD Candidate)
3TU Big Software on the Run
Millions of lines of code - written in different languages by different people at different times, and operating on a variety of platforms - drive the systems performing key processes in our society. The resulting software needs to evolve and can no longer be controlled a priori as is illustrated by a range of software problems. The 3TU.BSR research program will develop novel techniques and tools to analyze software systems in vivo - making it possible to visualize behavior, create models, check conformance, predict problems, and recommend corrective actions.
Staff involved
- Boudewijn van Dongen (Associate professor)
- Nour Assy (postDoc)
- Maikel Leemans (PhD Candidate)
- Cong Liu (PhD Candidate)
Philips Flagship
The Data Science Centre Eindhoven (DSC/e) is TU/e's response to the growing volume and importance of data and the need for data & process scientists. The DSC/e has recently started a long-term strategic cooperation with Philips Research Eindhoven on three topics: data science, health and lighting.
Staff involved
- Boudewijn van Dongen (Associate professor)
- Natalia Sidorova (Assistant professor)
- Alok Dixit (PhD Candidate)
- Bart Hompes (PhD Candidate)
- Niek Tax (PhD Candidate)
Process Mining in Logistics at Vanderlande
Logistics processes are notoriously difficult to design, analyze, and to improve. Where classical processes are scoped around the processing of information associated to a specific unique case, logistics deals with physical objects that are grouped and processed together with other physical objects in one process at one or more physical locations, then distributed and later on re-aggregated with other physical objects in another process at other physical locations. In essence, logistics deals with numerous processes, cases, and objects that interact with each other in a multi-dimensional fashion. On one hand, this subjects logistics processes to many external influences which can have a negative impact on process outcomes and process performance. On the other hand, when analyzing the performance of flows across networks of logistics, the multi-dimensional nature is especially prevalent and existing data-driven process analysis techniques such as process mining which assume a single viewpoint cannot be applied.
Staff involved
- Wil van der Aalst (Full professor)
- Boudewijn van Dongen (Associate professor)
- Dirk Fahland ((Assistant professor)
- Vadim Denisov (PhD Candidate)