Abstract
It is a central idea of BPM that processes are explicitly defined, then executed, and that information about process execution is prepared and analyzed. In this way, this information provides a feedback loop on how the process might be redesigned. Data about the execution of processes can stem from BPMSs in which processes are specified, but also from systems that do not work with an explicit process model, for instance ERP systems or ticketing systems. Data from those systems have to be transformed to meet the requirements of intelligent process execution analysis. This field is typically referred to as process mining.
This chapter deals with intelligently using the data generated from the execution of the process. We refer to such data as event logs, covering what has been done when by whom in relation to which process instance. First, we investigate the structure of event logs, their relationship to process models, and their usefulness for process monitoring and controlling. Afterwards, we discuss three major objectives of intelligent process analysis, namely transparency, performance and conformance. We discuss automatic process discovery as a technical step to achieve transparency of how the process is executed in reality. Then, we study how the analysis of event logs can provide insights into process performance. Finally, we discuss how the conformance between event logs and a process model can be checked.
If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.
H. James Harrington (1929–)
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Notes
- 1.
For simplicity, we only consider one supplier in this example.
- 2.
The software is available at http://www.promtools.org.
- 3.
Note that the α-algorithm was originally defined for constructing Petri nets. The version shown here is a simplification based on the five simple control flow patterns of Fig. 10.5 in order to construct BPMN models.
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Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A. (2013). Process Intelligence. In: Fundamentals of Business Process Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33143-5_10
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