Towards IoT-Driven Predictive Business Process Analytics

Abstract

Predictive business process monitoring is concerned with predicting the process-related Key Performance Indicators (KPIs) and forecasting the future behavior of the process in realtime. Despite the amount of work contributed by researches to this field of research, the performance of existing solutions is not desirable for practical settings. Indeed, these approaches are typically context-unaware and lack generality. However, in real-life use cases, business processes are not isolated from the surrounding working environment, and thus they are influenced by many contextual events, such as events generated by IoT devices. To the best of our knowledge, there is no comprehensive study addressing the integration of contextual events with the process prediction. This paper proposes a holistic context-aware methodology for predictive process monitoring by incorporating IoT data. Moreover, we present a systematic method to integrate the contextual events in the runtime process using Business Process Management System} (BPMS) capabilities. We also introduce a predictive model based on Deep Neural Networks (DNN) to forecast the next activity. Finally, we evaluate our solution using a case study in the aviation industry.