Work

Towards Run-Time Adaptation of Complex Event Forecasting

Year: 2025

Type: conference-paper

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15629357

Authors: Pitsikalis, Manolis, Alevizos, Elias, Giatrakos, Nikos, Artikis, Alexander

Institutions: National Centre of Scientific Research "Demokritos", The American College of Greece, Athena Research and Innovation Center In Information Communication & Knowledge Technologies, Technical University of Crete, University of Piraeus

Language: English

Topic: Advanced Database Systems and Queries

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: Critical Action Planning over Extreme-Scale Data