Process Modeling Recommender Systems

Abstract

The manual construction of business process models is a time-consuming, error-prone task and presents an obstacle to business agility. To facilitate the construction of such models, several modeling support techniques have been suggested. However, while recommendation systems are widely used, e.g., in e-commerce, these techniques are rarely implemented in process modeling tools. The creation of such systems is a complex task since a large number of requirements and parameters have to be taken into account. In order to improve the situation, the authors have developed a data model that can serve as a backbone for the development of process modeling recommender systems (PMRS). This article outlines the systematic development of this model in a stepwise approach using established requirements and validates it against a data model that has been reverse-engineered from a real-world system. In a last step, the paper illustrates an exemplary instantiation of the data model in a Smart Glasses-based modeling environment and discusses business process agility issues. The authors expect their contribution to provide a useful starting point for designing the data perspective of process modeling recommendation features that support business agility in process-intensive environments.