Rule-based complex event processing for food safety and public health

Authors:
Monica L. Nogueira;Noel P. Greis
Affiliations:
Kenan-Flagler Business School, The University of North Carolina at Chapel Hill, Chapel Hill, NC;Kenan-Flagler Business School, The University of North Carolina at Chapel Hill, Chapel Hill, NC
Venue:
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
Year:
2011

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Abstract

The challenge for public health officials is to detect an emerging foodborne disease outbreak from a large set of simple and isolated, domainspecific events. These events can be extracted from a large number of distinct information systems such as surveillance and laboratory reporting systems from health care providers, real-time complaint hotlines from consumers, and inspection reporting systems from regulatory agencies. In this paper we formalize a foodborne disease outbreak as a complex event and apply an event-driven rulebased engine to the problem of detecting emerging events. We define an evidence set as a set of simple events that are linked symptomatically, spatially and temporally. A weighted metric is used to compute the strength of the evidence set as a basis for response by public health officials.