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.
Smart Journey Mining: Towards successful digitalisation of services
The digitalisation of our society’s service systems has fundamentally changed the way services are delivered to, and experienced by, humans. Although digital services are supposed to simplify our lives and increase our efficiency, they often frustrate and burden customers, users, and employees. The overall goal is to increase the quality of services and support the successful digitalisation of services by uniting research on process mining and customer journeys using new developments in logic-based analysis and artificial intelligence. The partners are SINTEF Digital, University of Oslo, and Eindhoven University of Technology.
Staff involved
- Boudewijn van Dongen (professor)
- Felix Mannhardt (assistant professor)
- Marwan Hassani (assistant professor)
- Istvan Ketyko (PhD Candidate)
- and many others
Certification of production process quality through Artificial Intelligence
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production problems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production processes and techniques from artificial intelligence that can predict how the new process is likely to behave in practice in terms of data that its machines generate. This is especially important in mass customization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project.
Staff involved
- Boudewijn van Dongen (professor)
- Renato Calzone (program manager JADS)
- Natalia Sidorova (assistant professor)
- Dominique Sommers (PhD Candidate)
- Remco Dijkman (professor at TU/e)
- Eric Postma (professor at Tilburg University)
- Jeroen Middelhuis (PhD Candidate TU/e)
- Gabrial Raya (PhD Candidate Tilburg University)
- Roland Bijvank (Lecturer Utrecht University of Applied Sciences)
- and many others
Business Process Re-engineering and functional toolkit for GDPR compliance
The goal of BPR4GDPR is to provide a holistic framework able to support end-to-end GDPR-compliant intra- and interorganisational ICT-enabled processes at various scales, while also being generic enough, fulfilling operational requirements covering diverse application domains. To this end, proposed solutions will have a strong semantic foundation and cover the full process lifecycle addressing major challenges and priorities posed by the regulation, including requirements interpretation, broad territorial scope, accountability, security means enforcement, data subject’s rights and consent, unified data view and processing actions inventory, privacy by design, etc.
Staff involved
- Boudewijn van Dongen (professor)
- Marwan Hassani (assistant professor)
- Renata Medeiros de Carvalho (assistant professor)
- Azadeh Mozafari Mehr (PhD Candidate)
- Rashid Zaman (PhD Candidate)
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)