CONFIGURABLE PROCESS MODELS: EXPERIENCES FROM A MEDICAL AND AN ADMINISTRATIVE CASE STUDY

Reference (process) models are conceptual models that illustrate generic solutions for a certain domain. However, the generic concept of the reference process models requires the adaption of these process models to the demands of the individual customers. The concept of configurable process models is a step forwards towards this adaptation. Configurable process models integrate different variants of a business process into a single model. The deviation of an individual process model from a reference model can be seen as a variant of that reference model. While a technique for such models was already suggested in previous research, this paper presents two case studies in which this technique was tested in real-world scenarios. We sourced our material from a project at a middlesized hospital introducing “clinical pathways”, and from our own university documenting the “student-life-cycle”. For each of these projects we identified the variants including all of the variation points. The variation points mark the differences between the variants. After modeling, each identified set of variants was integrated into a configurable process model and these models were configured accordingly. This paper reports on both the observation during the model creation and the subsequent configuration

A framework for efficiently mining the organisational perspective of business processes

Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, among other things, with the assignment of human resources to process activities. Information about the resources that are involved in process activities can be mined from event logs in order to discover resource assignment conditions, which is valuable for process analysis and redesign. Prior process mining approaches in this context present one of the following issues: (i) they are limited to discovering a restricted set of resource assignment conditions; (ii) they do not aim at providing efficient solutions; or (iii) the discovered process models are difficult to read due to the number of assignment conditions included. In this paper we address these problems and develop an efficient and effective process mining framework that provides extensive support for the discovery of patterns related to resource assignment. The framework is validated in terms of performance and applicability

Mining team compositions for collaborative work in business processes

Process mining aims at discovering processes by extracting knowledge about their different perspectives from event logs. The resource perspective (or organisational perspective) deals, among others, with the assignment of resources to process activities. Mining in relation to this perspective aims to extract rules on resource assignments for the process activities. Prior research in this area is limited by the assumption that only one resource is responsible for each process activity, and hence, collaborative activities are disregarded. In this paper, we leverage this assumption by developing a process mining approach that is able to discover team compositions for collaborative process activities from event logs. We evaluate our novel mining approach in terms of computational performance and practical applicability

Mining Resource Assignments and Teamwork Compositions from Process Logs

Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, among other things, with the assignment of human resources to process activities. Information about the resources that are involved in process activities can be mined from event logs in order to discover resource assignment conditions. This is valuable for process analysis and redesign. Prior process mining approaches in this context present one of the following issues: (i) they are limited to discovering a restricted set of resource assignment conditions; (ii) they are not fully efficient; (iii) the discovered process models are difficult to read due to the high number of assignment conditions included; or (iv) they are limited by the assumption that only one resource is responsible for each process activity and hence, collaborative activities are disregarded. To overcome these issues, we present an integrated process mining framework that provides extensive support for the discovery of resource assignment and teamwork patterns