Semi-automatic inductive construction of reference process models that represent best practices in public administrations: A method

Scholta Hendrik, Niemann Marco, Delfmann Patrick, Räckers Michael, Becker Jörg

Abstract

Business process management often uses reference models to improve processes or as starting point when creating individual process models. The current academic literature offers primarily deductive methods with which to develop these reference models, although some methods develop reference models inductively from a set of individual process models, focusing on deriving and representing common practices. However, there is no inductive method with which to detect best practices and represent them in a reference model. This paper addresses this research gap by proposing a method by which to develop reference process models that represent best practices in public administrations semi-automatically and inductively. The method uses a merged model that retains the structure of the source models while detecting their common parts. It identifies best practices using query constructs and ranking criteria to group the source models’ elements and to evaluate these groups. We provide a conceptualization of the method and demonstrate its functionality using an artificial example. We describe our implementation of the method in a software prototype and report on its evaluation in a workshop with domain and method experts who applied the method to real-world process models.