Logic-based representation, reasoning and machine learning for event recognition

Authors:
Alexander Artikis;Georgios Paliouras;François Portet;Anastasios Skarlatidis
Affiliations:
Institute of Informatics & Telecommunications, Athens, Greece;Institute of Informatics & Telecommunications, Athens, Greece;Grenoble Universités, Saint Martin d'Hères, France;Institute of Informatics & Telecommunications, Athens, Greece and University of the Aegean, Greece
Venue:
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Year:
2010

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Abstract

Today's organisations require techniques for automated transformation of the large data volumes they collect during their operations into operational knowledge. This requirement may be addressed by employing event recognition systems that detect activities/events of special significance within an organisation, given streams of 'low-level' information that is very difficult to be utilised by humans. Numerous event recognition systems have been proposed in the literature. Recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention because, among others, they exhibit a formal, declarative semantics, they haven proven to be efficient and scalable, and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper we review representative approaches of logic-based event recognition, and discuss open research issues of this field.