Data Science Center Eindhoven
Description
Recent technological and societal changes led to an explosion of digitally available data. Exploiting the available data to its fullest extent, in order to improve decision making, increase productivity, and deepen our understanding of scientific questions, is one of today’s key challenges. Data science is an emerging area that aims to address this challenge. It is a multi-disciplinary area, where computer science and mathematics play crucial roles. The Graduate Program on Data Science leverages the presence at the TU/e of excellent research groups in the data-science area, and to give highly talented students the opportunity to be educated in and contribute to this exciting area. The positions are funded by the NWO Graduate Program.
The Graduate Program on Data Science is part of the Data Science Center Eindhoven (DSC/e), launched in December 2013. It builds on the excellence of several research groups within the department that together cover many of the core topics in data science: algorithms, visualization, data mining, process mining, statistics and probability, stochastics, operations research, and optimization. This ensures a stimulating and excellent environment for the selected students.
The projects fall at the intersection of computer science and mathematics, and are expected to open up promising connections between these fields. Together with the intended supervisors from the relevant research group(s), the students will have the opportunity to define their own research project. The overall aim is to make fundamental advances in the area of Data Science.
Links
Publications
- Hierarchical performance analysis for process mining - Leemans, Maikel, Van Der Aalst, Wil M.P. & Van Den Brand, Mark G.J. (2018). Hierarchical performance analysis for process mining. Proceedings of the 2018 International Conference on Software and System Process, ICSSP 2018 (pp. 96-105). Association for Computing Machinery, Inc. Abstract Process mining techniques use event data from operational and software processes to discover process Read More ...
- The Statechart Workbench : Enabling scalable software event log analysis using process mining - Leemans, Maikel, van der Aalst, Wil M.P. & van den Brand, Mark G.J. (2018). The Statechart Workbench : Enabling scalable software event log analysis using process mining. 25th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2018 – Proceedings (pp. 502-506). Institute of Electrical and Electronics Engineers (IEEE). Abstract To understand and maintain Read More ...
- Recursion aware modeling and discovery for hierarchical software event log analysis - Leemans, Maikel, van der Aalst, Wil M.P. & van den Brand, Mark G.J. (2018). Recursion aware modeling and discovery for hierarchical software event log analysis. 25th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2018 – Proceedings (pp. 185-196). Piscataway: Institute of Electrical and Electronics Engineers (IEEE). Abstract This paper presents 1) a Read More ...
- Software process analysis methodology-a methodology based on lessons learned in embracing legacy software - Leemans, Maikel, van der Aalst, Wil M.P., van den Brand, Mark G.J., Schiffelers, Ramon R.H. & Lensink, Leonard (2018). Software process analysis methodology-a methodology based on lessons learned in embracing legacy software. Proceedings – 2018 IEEE International Conference on Software Maintenance and Evolution, ICSME 2018 (pp. 665-674). Piscataway: Institute of Electrical and Electronics Engineers (IEEE). Read More ...
- Hierarchical process mining for scalable software analysis - Leemans, M. (2018). Hierarchical process mining for scalable software analysis. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Wil van der Aalst & Mark van den Brand).
Former staff
- Maikel Leemans - Maikel is a PhD student within the PA group where his main research is in the area of process mining. More concretely, his research interests focus on the analysis of software, where he looks at finding common usage patterns, possible deviations and options how to improve the usage process by changing the software. Position: PhD Read More ...
- Wil van der Aalst - Prof.dr.ir. Wil van der Aalst is a full professor of the Process and Data Science (PADS) group at the RWTH in Aachen (Germany) and a part-time professor in the PA group. His personal research interests include process mining, business process management, workflow management, Petri nets, process modeling, and process analysis. Position: HGL Room: MF 7.064 Read More ...