Research in the domain of process mining has contributed significantly to evidence-based analysis of business processes from the control flow perspective, e.g., process discovery (which focuses on discovering the temporal patterns between activities) and conformance checks (which often focus on the alignment of activities between prescribed and recorded behaviours). Nevertheless, business processes consist of not only control flow perspective, but also other perspective, including resource and data perspectives.
The goal of our research in the area of resource behaviour mining is to provide a set of techniques and tools that can be used to extract insights related to resources’ behaviours by exploiting event logs.
The main question in this type of research is “How do resources work?”. More precisely, we attempt to answer the following questions:
- Do resources exhibit some patterns in the way they work, e.g., the way in which work items are distributed or the `strategy’ that resources apply in selecting the work items to perform?
- Do resources change their working patterns over time?
- How can we profile resources behaviours? If so, do they have an impact on the outcomes of processes?
- How can we optimize the deployment and the scheduling of resources based on historical data to improve productivity?
We have conducted research in this area within the context of the Risk-aware Business Process Management project (DP110100091) and Cost-aware Business Process Management project (DP120101624). These projects have produced a number of research outcomes, as summarized below.
A. Pika, W.M.P. van der Aalst, M.T. Wynn, C.J. Fidge, and A.H.M. ter Hofstede. Evaluating and predicting overall process risk using event logs. Information Sciences, 352-353, pp. 98-120, 2016.
A. Pika, W. M. P. van der Aalst, C. Fidge, A. H. M. ter Hofstede and M. T. Wynn. Profiling Event Logs to Configure Risk Indicators for Process Delays. In Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE), vol 7908, pp. 465-481. LNCS, Springer, 2013. Published Version. Technical Report.
A. Pika, M. T. Wynn, C. Fidge, A. H. M. ter Hofstede, M. Leyer, and W. M. P. van der Aalst. An Extensible Framework for Analysing Resource Behaviour Using Event Logs. In Proceedings of the 26th International Conference of Advanced Information Systems Engineering (CAiSE), vol 8484, pp. 564-579. LNCS, Springer, 2014. Published Version. Technical Report.
S. Suriadi, C. Ouyang, W. M. P. van der Aalst, A. H. M. ter Hofstede. Root cause analysis with enriched process logs. In Business Process Management Workshops, vol 132, pp. 174-186. LNBIP, Springer, 2013. Published Version. Technical Report.
A. Pika, W. M. P. van der Aalst, C. Fidge, A. H. M. ter Hofstede, M. T. Wynn. Predicting Deadline Transgressions Using Event Logs. In Business Process Management Workshops, vol 132, pp. 211-216. LNBIP, Springer, 2013. Published Version. Technical Report.