The RALph miner for automated discovery and verification of resource-aware process models
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SPECIAL SECTION PAPER
The RALph miner for automated discovery and verification of resource-aware process models Cristina Cabanillas1,2 · Lars Ackermann3 · Stefan Schönig4 · Christian Sturm3 · Jan Mendling2 Received: 14 December 2018 / Revised: 14 July 2020 / Accepted: 22 July 2020 © The Author(s) 2020
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. Keywords Model checking · Organisational mining · Process mining · Process verification · RALph · Resource assignment · Resource mining
1 Introduction Communicated by Rainer Schmidt and Jens Gulden. This work was funded by the Austrian Science Fund (FWF)—Grant V 569-N31 (PRAIS); and by MCI/AEI/FEDER, UE—Grant RTI2018-100763-J-100 (CONFLEX).
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Cristina Cabanillas [email protected] Lars Ackermann [email protected] Stefan Schönig [email protected] Christian Sturm [email protected] Jan Mendling [email protected]
1
University of Seville, Seville, Spain
2
Vienna University of Economics and Business, Vienna, Austria
3
University of Bayreuth, Bayreuth, Germany
Process mining extracts relevant information on executed business processes from historical data stored in event logs and analyses it for different purposes [59]. Process discovery organises the information extracted in the form of a process model. The richer the data in the event logs, the more facets of the underlying processes that can be discovered. Such event data typically refers to the executed activities, their timestamps and the human resources (for short resources) that were involved. The functional
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