Profile

This project aims at developing novel approaches and techniques for modern Process-Aware Information Systems (PAIS) by integrating Business Process Management (BPM) and IoT Big Data technologies. Enabled by IoT, BPM can perceive real-time physical world events, which results in faster and better decisions reacting to them. At the same, the ever-increasing IoT big data can be effectively leveraged and programmed by means of business processes. Focusing on the scientific research challenge of paradigm misalignment between BPM and IoT, this project plans to deal with three fundamental issues, namely, mismatch of programming mechanisms, mismatch of data persistence mechanisms, and mismatch of ad hoc processes and overall control, to make breakthroughs in the abstraction of IoT data and events, proactive programming, and instantaneous process adaptability induced by IoT events, so as to make traditional BPM more proactive. A series of test cases and a set of benchmarks will also be validated based on the typical application scenarios from Adesso SE in Germany. The main innovative contributions include:

1) an enriched BPM modeling approach with novel abstracting and programming mechanisms to facilitate the induction of IoT big data, modeling of proactive data services, and an IoT-enabled micro-process model to realize situation-aware IoT big data processing logic;

2) the topological models for IoT-enabled micro-processes, their organization and evolution mechanisms. To this end, a massive amount of ad hoc micro-processes will be organized and managed in a controllable manner;

3) a data persistence model that reconciles stream data and batch data, which contributes to unified management and query in decentralized settings;

4) an agile solution to long-tail variants of business processes that cannot be properly handled by traditional process adaptation, and the controllability verification against some key performance indicators (KPIs). All the above achievements will support proactive service routing and evolutionary learning driven by IoT-related unpredictable events and situations.