Context-aware Predictive Process Analytics (CoPPA)

• Applicants: Professor Dr. Patrick Delfmann; Professor Dr. Martin Matzner
• Subject Area: Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 456415646

Process analytics is now a widely used business application. Two decades of fundamental research on techniques and algorithms set the stage for real-world ex-post analysis of process data. Both Celonis SE, the market-leading vendor, and Siemens, the largest user ofprocess analytics software are located in Germany. The research described in this proposal deals with the next logical level of process analytics: predicting the behavior of future process instances. The predictive power of current approaches to predictive process analytics, most of which operate exclusively on process-event log data, is close to random. Therefore, context-aware predictive process analytics (CoPPA) explores opportunities to include process-context data in predictions of future process behavior. The project builds upon prior research of the two proposers that was published in MIS Quarterly and is support through data provision by industrial partners. We design a context-based predictive analytics technique using probabilistic models from the class of Dynamic Bayesian Networks (DBN) on that basis. We understand context as every aspect related to a business process that goes beyond the sheer name of an activity. Hence, context includes, for instance, input and output data of an activity, people performing an activity, and sensor data characterizing the environment of a process. Context can significantly influence the behavior of a process, so we can expect that being aware of what context has been present in a process instance can increase the prediction accuracy of a predictive process monitoring approach (e.g., the amount of a loan in a loan approval process may have a considerable influence onto the subsequent decisions made in the process and thus influences its behavior).At the end of the envisioned CoPPA project, we expect to provide four deliverables: novel predictive process analytics models and training algorithms, novel algorithms that make predictions on running process instances, novel predictive process analytics visualization concepts, and a scientific workflow environment that can be used to set up, run, and evaluate predictive process analytics scenarios. All of these outcomes will come with extensive sets of evaluation metrics, so we will make multiple contributions to the scientific body of knowledge related to process analytics. All created artifact will be published under an open-source software license and they will be made publicly accessible.

• DFG Programme: Research Grants