With the increasing importance of business processes as competitive differentiators for organisations, data analytics and data mining are becoming the tools to “wring every last drop of value from these processes. Process mining (van der Aalst 2016), a branch of data science that bridges the gap between data mining and traditional forms of process analysis, provides analytical tools and methods which can deal with the huge volume of process-related data and distil insights about existing processes for decision makers.
The idea of process mining is to discover, monitor and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today’s (information) systems. Process mining includes (automated) process discovery (i.e., extracting process models from an event log), conformance checking (i.e., monitoring deviations by comparing model and log), social network/organizational mining, automated construction of simulation models, model extension, model repair, case prediction, and history-based recommendations.
With the advancement of tools and techniques, process mining may cover different perspectives. The control-flow perspective focuses on the control-flow, i.e., the ordering of activities. The goal of mining this perspective is to find a good characterization of all possible paths. The result is typically expressed in terms of a Petri net or some other process notation (e.g., EPCs, BPMN, or UML activity diagrams). The organizational perspective focuses on information about resources hidden in the log, i.e., which actors (e.g., people, systems, roles, or departments) are involved and how are they related. The case perspective focuses on properties of cases.
Accordingly process mining can be applied through different stages of Business Process Life Cycle. These capabilities also enables process mining researchers to go beyond business process management and use these techniques in other areas of research as well. We are interested to understand the evolution of process mining and its impact on different areas of research, to be able to tangibilize process mining impact and identify the opportunities for further progress of the field.
The research team has collected a data base of process mining case studies from 2005 to 2018. You are expected to review these case studies and answer the following questions:
- How these studies can be positioned according to different stages of BPM life cycle? How the outcomes of studies can be evaluated according to the expected outcomes for the corresponding stages?
- Other than BPM, what are the other purposes/areas of studies in which process mining has been used?
After this investigation you will expand the case studies to include the ones that process mining is used in combination with other techniques, and you will investigate the above questions in this new data set as well.
The outcome of this study is a report or possibly a paper on the content analysis findings. The results of this study will show the evolution of process mining studies and also the potential opportunities of this field in the new areas.
The outcomes also can be used to define measures of success or impacts of a process mining project;(this will be done in collaboration with other team members).
Skills and experience
- A basic understanding of business process management and process modeling
- Critical and analytical thinking skills.
Contact the supervisor for more information.