Technische Universiteit Eindhoven

Technische Universiteit Eindhoven

The Department of Mathematics and Computer Science of Eindhoven University of Technology strives to be leading in the science and engineering of software systems. It focuses on generic aspects of the design of software systems. In particular, focus is on the following two related and complementary themes: Design methods for large-scale, reliable software systems and Analysis of software systems.

The Model Driven Software Engineering (MDSE) section contributes to this high quality research by combining model based software engineering and formal modeling and verification techniques to improve the efficiency and the quality of the software development process.

LaQuSo (Laboratory for Quality Software) provides access to a wide range of research groups and their social and technical infrastructure, in particular on various aspects of model driven software engineering, secure and embedded networked systems, and algorithms and visualization.

TUE is involved in the tasks related to the development of the modeling and design frameworks, in particular those related to definition of model-to-model transformation. In addition, TUE will take an active role in the validation of the project approach.

Information and embedded systems are becoming more and more intertwined with the operational processes in most organizations. As a result, a multitude of events are generated and/or recorded by today's systems. The goal of process mining is to use these event data to extract process-related information, e.g., to automatically discover a process model by observing events recorded by some enterprise system. Moreover, more and more devices are connected to the Internet today, thus allowing for unprecedented streams of data. An example is the “CUSTOMerCARE Remote Services Network” of Philips Healthcare (PH). This is a worldwide internet-based private network that links PH equipment to remote service centers. Any event that occurs within an X-ray machine (e.g., moving the table, setting the deflector, etc.) is recorded and can be analyzed. The logging capabilities of the machines of PH illustrate the increasing availability of event data. Another example is the use of RFID (Radio-Frequency IDentification). RFID tags are used in passports, access cards, retail stores, inventory systems, supply chains, etc. The anticipated widespread adoption of this technology will result in even more event data. Since we are interested in analyzing processes based on the data recorded, we focus on events that can be linked to relevant activities. The order of such events is important for deriving the actual process. Fortunately, most events have a timestamp or can be linked to a particular date.

Process mining techniques attempt to extract non-trivial and useful information from event logs. One aspect of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net or BPMN model) describing the causal dependencies between activities. Process mining is not limited to control-flow discovery. In fact, we identify three types of process mining: (a) discovery, (b) conformance, and (c) extension. We also distinguish three different perspectives: (a) the control-flow perspective (“How?”), (b) the organizational perspective (“Who?” or which resource) and (c) the case perspective (“What?”).