Abstract
Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emphasis on (human) resources, aiming at constructing resource-aware process models that contain the inferred resource assignment constraints. Such constraints can be complex and process discovery approaches so far have missed the opportunity to represent expressive resource assignments graphically together with process models. A subsequent verification of the extracted resource-aware process models is required in order to check the proper utilisation of resources according to the resource assignments. So far, research on discovering resource-aware process models has assumed that models can be put into operation without modification and checking. Integrating resource mining and resource-aware process model verification faces the challenge that different types of resource assignment languages are used for each task. In this paper, we present an integrated solution that comprises (i) a resource mining technique that builds upon a highly expressive graphical notation for defining resource assignments; and (ii) automated model-checking support to validate the discovered resource-aware process models. All the concepts reported in this paper have been implemented and evaluated in terms of feasibility and performance.
Highlights
Process mining extracts relevant information on executed business processes from historical data stored in event logs and analyses it for different purposes [59]
Providing support for the eight analysis operations that we have found formally defined in the literature constitute the model-checking (MC) requirements in this work, in particular: (MC1) Support for the Potential Participants (PP) operation, which infers the resources that can participate in a process activity given the resource assignments in the process model
Since the current paper focuses on model checking rather than example generation the details for the latter are skipped
Summary
Process mining extracts relevant information on executed business processes from historical data stored in event logs and analyses it for different purposes [59]. Most of the recent process mining techniques focus on the two former perspectives and generate textual as well as graphical representations of the discovered processes [23,45] The target of those approaches have been both routine (or procedural) processes, which are usually modelled with imperative notations (e.g., Business Process Model and Notation (BPMN) [39]); and flexible processes, for which declarative notations are preferred (e.g., Declare [58]). A key challenge of approaches that yield rules, such as Declare constraints, based on support thresholds can produce rule sets that are inconsistent For this reason, the constructed process models require an additional check in order to avoid or fix conflicting constraints [21]. Regarding activities and control flow, a number of approaches to check process soundness [42] have been developed (e.g., [14,33])
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