Christian W. Günther

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Process Mining is a technique for extracting process models from execution logs. This is particularly useful in situations where people have an idealized view of reality. Real-life processes turn out to be less structured than people tend to believe. Unfortunately, traditional process mining approaches have problems dealing with unstructured processes. The(More)
This tool paper describes the functionality of ProM. Version 4.0 of ProM has been released at the end of 2006 and this version reflects recent achievements in process mining. Process mining techniques attempt to extract non-trivial and useful information from so-called “event logs”. One element of process mining is control-flow discovery, i.e.,(More)
Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being(More)
Process mining includes the automated discovery of processes from event logs. Based on observed events (e.g., activities being executed or messages being exchanged) a process model is constructed. One of the essential problems in process mining is that one cannot assume to have seen all possible behavior. At best, one has seen a representative subset.(More)
Nowadays, all kinds of information systems store detailed information in logs. Process mining has emerged as a way to analyze these systems based on these detailed logs. Unlike classical data mining, the focus of process mining is on processes. First, process mining allows us to extract a process model from an event log. Second, it allows us to detect(More)
Process mining has proven to be a valuable tool for analyzing operational process executions based on event logs. Existing techniques perform well on structured processes, but still have problems discovering and visualizing less structured ones. Unfortunately, process mining is most interesting in domains requiring flexibility. A typical example would be(More)
The application of process mining techniques to real-life corporate environments has so far been of an ad-hoc nature, focused on proving the concept. One major reason for this rather slow adoption has been the complicated task of transforming real-life event log data to the MXML format used by advanced process mining tools, such as ProM. In this paper, the(More)
Today there are many process mining techniques that allow for the automatic construction of process models based on event logs. Unlike synthesis techniques (e.g., based on regions), process mining aims at the discovery of models (e.g., Petri nets) from incomplete information (i.e., only example behavior is given). The more mature process mining techniques(More)
More and more information about processes is recorded by information systems in the form of so-called “event logs”. Despite the omnipresence and richness of these event logs, most software vendors have been focusing on relatively simple questions under the assumption that the process is fixed and known, e.g., the calculation of simple performance metrics(More)