Towards Situational Reference Model Mining - Main Idea, Procedure Model & Case Study

This contribution introduces the concept of Situational Reference Model Mining, i.e. the idea that automatically derived reference models, although based on the same input data, are intended for different use cases and thus have to meet different requirements. These requirements determine the reference model character and thus the technique that is best suited for mining it. Situational Reference Model Mining is based on well-known design principles for reference modeling, such as configuration, aggregation, specialization, instantiation, and analogy. We present a procedure model for Situational Reference Model Mining and demonstrate its usefulness by means of a case study. Existing techniques for Reference Model Mining are examined and mapped to their underlying design principles. This way, we are not only able to provide reference model designers with concrete guidelines regarding their choice of mining technique, but also point out research gaps for the development of new approaches to reference model mining

Towards Situational Reference Model Mining - Main Idea, Procedure Model & Case Study

This contribution introduces the concept of Situational Reference Model Mining, i.e. the idea that automatically derived reference models, although based on the same input data, are intended for different use cases and thus have to meet different requirements. These requirements determine the reference model character and thus the technique that is best suited for mining it. Situational Reference Model Mining is based on well-known design principles for reference modeling, such as configuration, aggregation, specialization, instantiation, and analogy. We present a procedure model for Situational Reference Model Mining and demonstrate its usefulness by means of a case study. Existing techniques for Reference Model Mining are examined and mapped to their underlying design principles. This way, we are not only able to provide reference model designers with concrete guidelines regarding their choice of mining technique, but also point out research gaps for the development of new approaches to reference model mining

Leveraging artificial intelligence for business process management (extended abstract) : A contribution to reference model mining, predictive process monitoring, and process discovery

Artificial intelligence for business process managmen

AN EXECUTION-SEMANTIC APPROACH TO INDUCTIVE REFERENCE MODEL DEVELOPMENT

Reference models are a cost- and time-saving approach for the development of new models. As induc-tive strategies are capable of automatically deriving a potential reference process model from a col-lection of existing process models, they have gained attention in current research. A number of prom-ising approaches can be found in recent publications. However, all existing methods rely on graph-based similarity measures to identify commonalities between input models. Since behaviourally simi-lar process models can have different graphical structures, those approaches are unable to find cer-tain commonalities. To overcome these shortcomings, we propose a new approach to inductive refer-ence model development based on an execution-semantic similarity measure. Since a naïve solution to the intuitive idea does not yield productive results, the proposed approach is rather elaborate. By cap-turing the commonalities of the input models in a behavioural profile, we are able to derive a refer-ence model subsuming the input models’ semantics instead of their structure. In our contribution, this approach is outlined, implemented and evaluated in three different scenarios. As the evaluations show, it is capable of handling complex process models and overcome most restrictions that structural ap-proaches pose. Thus, it introduces a new level of flexibility and applicability to inductive reference modelling

Model-Based Compliance in Information Systems – Foundations, Case Description and Data Set of the MobIS-Challenge for Students and Doctoral Candidates

Information systems (IS) can significantly support the organization of business processes. However, the proceeding digitalization of processes can also lead to an increasing organizational complexity and the need to more intensely investigate the adherence to external or internal compliance rules. Process-related data from IS and underlying process models can, however, also contribute to an effective compliance checking. This paper first presents conceptual foundations of model-based compliance checking that motivated the MobIS-Challenge workshop for students and doctoral candidates at WI 2019.Second, we introduce the challenge itself and its corresponding data set. The data describes an exemplary travel management process in a medium-sized consulting company and served for the development and validation of adequate solutions addressing the compliance checking requirements. Solutions accepted for presentation at the workshop are briefly outlined in this paper

Process Mining Meets Visual Analytics: The Case of Conformance Checking

Conformance checking is a major function of process mining, which allows organizations to identify and alleviate potential deviations from the intended process behavior. To fully leverage its benefits, it is important that conformance checking results are visualized in a way that is approachable and understandable for non-expert users. However, the visualization of conformance checking results has so far not been widely considered in research. Therefore, the goal of this paper is to develop an understanding of how conformance checking results are visualized by process mining tools to provide a foundation for further research on this topic. We conduct a systematic study, where we analyze the visualization capabilities of nine academic and seven commercial tools by means of a visual analytics framework. In this study, we find that the ''Why?'' aspect of conformance checking visualization seems already be well-defined, but the ''What?'' and ''How?'' aspects require future research.Comment: Submitted to HICSS-56 (2023

Process Mining Meets Visual Analytics: The Case of Conformance Checking

Conformance checking is a major function of process mining, which allows organizations to identify and alleviate potential deviations from the intended process behavior. To fully leverage its benefits, it is important that conformance checking results are visualized in a way that is approachable and understandable for non-expert users. However, the visualization of conformance checking results has so far not been widely considered in research. Therefore, the goal of this paper is to develop an understanding of how conformance checking results are visualized by process mining tools to provide a foundation for further research on this topic. We conduct a systematic study, where we analyze the visualization capabilities of nine academic and seven commercial tools by means of a visual analytics framework. In this study, we find that the ``Why?'' aspect of conformance checking visualization seems already be well-defined, but the ``What?'' and ``How?'' aspects require future research