Publication
Situational Reference Model Mining
Jana-Rebecca Rehse
In: Marite Kirikova; Audrone Lupeikiene; Ernest Teniente (Hrsg.). CAiSE 2018 Doctoral Consortium Papers. CAiSE Doctoral Consortium (CAiSE-DC-2018), located at 30th International Conference on Advanced Information Systems Engineering (CAiSE 2018), June 11-15, Tallinn, Estonia, CEUR-WS, 2018.
Abstract
Reference models can be considered as special conceptual models that serve to be reused for the design of other conceptual models. Due to an ongoing need for high-quality reference models, reference model mining, i.e. the (semi-)automatic derivation of reference models from a set of existing process models, has recently gained the attention of researchers. The presented dissertation project addresses the concept of Situational Reference Model Mining, i.e. the idea that mined 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. The dissertation's major objective is to design, elaborate, and validate a method for Situational Reference Model Mining, which provides reference modelers with a clear guideline on how to use automated reference model mining techniques to their advantage.