A Complex Event Processing Approach for Crisis-Management Systems

TL;DR

This paper introduces a complex event processing approach for crisis management that correlates real-time events from diverse sources to detect critical situations efficiently, supporting emergency decision-making.

Contribution

It presents a novel online event correlation architecture leveraging complex event processing for improved crisis detection in emergency management systems.

Findings

Effective detection of critical situations through event correlation

Integration of crowd sensing and crowd sourcing data sources

Real-time analysis enhances emergency response capabilities

Abstract

In modern advanced emergency management systems many solutions for decision support have been provided as attempts to support humans to take important decisions for the critical situations recovery. The critical situation detection is a complex procedure that involves both human and machine activities and leads to take a decision for the management and situation recovery. This paper presents an approach for critical situation detection which uses event correlation technologies performing online analysis of real events through a Complex Event Processing architecture. Event correlation is used to relate events gathered from various sources, including crowd sensing and crowd sourcing sources, for detecting patterns and situations of interest in the emergency management context.