Enhancing Situation Awareness Using Semantic Web Technologies and Complex Event Processing

Havva Alizadeh Noughabi (), Mohsen Kahani () and Alireza Shakibamanesh ()
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Havva Alizadeh Noughabi: University of Gonabad
Mohsen Kahani: Ferdowsi University of Mashhad
Alireza Shakibamanesh: Ferdowsi University of Mashhad

Annals of Data Science, 2018, vol. 5, issue 3, No 10, 487-496

Abstract: Abstract Data fusion techniques combine raw data of multiple sources and collect associated data to achieve more specific inferences than what could be attained with a single source. Situational awareness is one of the levels of the JDL, a matured information fusion model. The aim of situational awareness is to understand the developing relationships of interests between entities within a specific time and space. The present research shows how semantic web technologies, i.e. ontology and semantic reasoner, can be used to describe situations and increase awareness of the situation. As the situation awareness level receives data streams from numerous distributed sources, it is necessary to manage data streams by applying data stream processor engines such as Esper. In addition, in this research, complex event processing, a technique for achieving related situational in real-time, has been used, whose main aim is to generate actionable abstractions from event streams, automatically. The proposed approach combines Complex Event Processing and semantic web technologies to achieve better situational awareness. To show the functionality of the proposed approach in practice, some simple examples are discussed.

Keywords: Situation awareness; Semantic reasoning; Data stream management; Complex event processing (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s40745-018-0148-1

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