Facilitating process analysis through visualising process history: experiences with a Dutch municipality

Nowadays vast quantities of data are stored as a result of the operation of software systems and devices. The analysis of this data can provide valuable insights. In the field of Business Process Management, event logs may provide valuable information for business process improvement. This is the realm of process mining, an area which has provided many analysis techniques over the past decade. Despite the abundance of process mining techniques, it remains a challenge to provide results that are understandable by domain experts. Discovered process models are often perceived as abstract and static. Conformance checking techniques provide detailed results that are only understandable for process analysts. Therefore, we propose an approach to dynamically visualize event data on intuitive 'maps'. States of the process are visualized on a collection of maps thus resulting in sequences of 'photographs' of the process under investigation. By replaying the event log using such visualizations we can create a collection of 'process movies'. Our visualisation approach has been implemented in ProM and allows for any type of 'map' as long as activity instances can be associated to map coordinates. Moreover, the approach has been evaluated in collaboration with a Dutch municipality

Mining process performance from event logs : the BPI Challenge 2012 case study

Having reliable performance information is often crucial in many business process improvement efforts. In systems where process executions are not strictly enforced by a predefined process model, obtaining such information is difficult. In this paper, we analyzed an event log of a real-life process, taken from a Dutch financial institute, using process mining techniques. In particular, we used the alignment technique to gain insights into the control flow and performance of the process execution. We showed that alignments between event logs and discovered process models from process discovery algorithms reveal frequent occurring deviations. Insights into these deviations can be exploited to repair the original process models to better reflect reality. Furthermore, we show that the projection of alignments onto process model provides reliable performance information. All analysis in this paper is performed using existing and dedicated plug-ins within the open-source process mining toolkit ProM

Loan application example, configuration 4

Loan application example, configuration 4 Parent item: Loan application example A collection of artificial event logs describing 4 variants of a simple loan application process. Variant 1 is the most complex process with parallelism and choices. The other 3 variants have a simpler, more sequential, control flow and some activities of variant 1 are missing or split into 2. These event logs are used to test different approaches of discovering a configurable process model from a collection of event logs