Extracting Reusable Fragments From Data-Centric Process Variants

Abstract

Data-centric process management supports knowledge workers in performing knowledge-intensive processes in a flexible way. An essential ingredient of data-centric process management are process templates that are manually modified for a specific case to suit the context of that case. Modifying templates results in many different yet related process variants. However, manually modifying a template is time consuming and may lead to errors. This paper defines an approach to extract reusable fragments from data-centric process variants. The set of extracted fragments is minimal. By composing the fragments not only the input variants but many more process variants can be derived. We have implemented the approach in a prototype and evaluated it on several business processes. Using the fragment extraction approach, complex data-centric process variants can be designed more efficiently and their quality can improve, since well-known modifications are applied.