Stefan Schönig

Lars Ackermann, Martin Käppel, Laura Marcus et al.

The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact BPM as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BPM.

Martin Käppel, Stefan Jablonski, Stefan Schönig

Predictive business process monitoring is concerned with the prediction how a running process instance will unfold up to its completion at runtime. Most of the proposed approaches rely on a wide number of different machine learning (ML) techniques. In the last years numerous comparative studies, reviews, and benchmarks of such approaches where published and revealed that they can be successfully applied for different prediction targets. ML techniques require a qualitatively and quantitatively sufficient data set. However, there are many situations in business process management (BPM) where only a quantitatively insufficient data set is available. The problem of insufficient data in the context of BPM is still neglected. Hence, none of the comparative studies or benchmarks investigates the performance of predictive business process monitoring techniques in environments with small data sets. In this paper an evaluation framework for comparing existing approaches with regard to their suitability for small data sets is developed and exemplarily applied to state-of-the-art approaches in predictive business process monitoring.

Lars Ackermann, Stefan Schönig, Stefan Jablonski

Process modeling is usually done using imperative modeling languages like BPMN or EPCs. In order to cope with the complexity of human-centric and flexible business processes several declarative process modeling languages (DPMLs) have been developed during the last years. DPMLs allow for the specification of constraints that restrict execution flows. They differ widely in terms of their level of expressiveness and tool support. Furthermore, research has shown that the understandability of declarative process models is rather low. Since there are applications for both classes of process modeling languages, there arises a need for an automatic translation of process models from one language into another. Our approach is based upon well-established methodologies in process management for process model simulation and process mining without requiring the specification of model transformation rules. In this paper, we present the technique in principle and evaluate it by transforming process models between two exemplary process modeling languages.