Managing Semantic Big Data for Intelligence
Anne-Claire Boury-Brisset · 2013
All-source intelligence production involves the collection and analysis of intelligence data provided in various formats (raw data from sensors, imagery, text-based from human reports, etc.) and distributed across heterogeneous data stores. The advance in sensing technologies, the acquisition of new sensors, and use of mobile devices result in the production of an overwhelming amount of sensed data, that augment the challenges to transform these raw data into useful, actionable intelligence in a timely manner. Leveraging recent advances in data integration, Semantic Web and Big Data technologies, we are adapting key concepts of unified dataspaces and semantic enrichment for the design and implementation of a R&D intelligence data integration platform MIDIS (Multi-Intelligence Data Integration Services). The development of this scalable data integration platform rests on the layered dataspace approach, makes use of recent Big Data technologies and leverages ontological models, and semantic-based analysis services developed for various purposes as part of the semantic layer. This paper describes ongoing research for the design and implementation of a prototype for scalable Multi-Intelligence Data Integration Services (MIDIS) in support of these objectives, based on a flexible data integration approach, making use of Semantic Web and Big Data technologies. The paper is organized as follows. In the next section, we present recent work addressing multi-intelligence data integration, followed by a short introduction to Big Data challenges. Section IV describes the proposed architecture for large-scale intelligence data integration and analysis and details the main components of the resulting architecture. Section V provides details about the implementation using Big Data technologies. Section VI provides some conclusions and directions for future work.