Towards Run-Time Adaptation of Complex Event Forecasting

Complex Event Forecasting (CEF) is a process whereby complex events of interest are forecast over a stream of simple events. CEF facilitates proactive measures by anticipating the occurrence of complex events. This proactive property, makes CEF a crucial task in many domains; for instance, in maritime situational awareness, forecasting the arrival of vessels at ports allows for better resource management, and higher operational efficiency. However, our world’s dynamic and evolving conditions necessitate the use of adaptive methods. For example, maritime vessels adapt their routes based on weather or human-caused factors. CEF systems typically rely on probabilistic models, trained on historical data. This renders such CEF systems inherently susceptible to data evolutions that can invalidate their underlying models. To address this problem, we propose RTCEF, a novel framework for Run-Time Adaptation of CEF, based on a distributed, service-oriented architecture. We evaluate RTCEF on a real-world maritime use-case and our reproducible results show that our proposed approach has significant benefits in terms of forecasting performance without sacrificing efficiency.