High-Level fusion for intelligence applications using Recombinant Cognition Synthesis

Marco A. Solano, Stephen Ekwaro-Osire, Murat M. Tanik

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Intelligence applications exploit heterogeneous data using High-Level fusion systems to gain information superiority. Whereas Low-Level fusion systems have well established frameworks, High-Level fusion has not yet achieved the same level of maturity. Most High-Level systems implement specialized algorithms that yield useful results, albeit for a very narrow input space, and are characterized by stove-pipe architectures and a fragmented workflow. Recombinant Cognition Synthesis bridges the implementation gap of existing fusion models by defining a comprehensive framework of semantic, temporal, and geospatial enablers comprising the primitives, functions, and models, which through a recombinant workflow, maximize the data exploitation value-chain. This paper presents a methodology and the underlying architectural components necessary to implement a unified High-Level fusion intelligence application, followed by a case study that demonstrates the resulting improvements in knowledge discovery and predictive accuracy.

Original languageEnglish
Pages (from-to)79-98
Number of pages20
JournalInformation Fusion
Volume13
Issue number1
DOIs
StatePublished - Jan 2012

Keywords

  • Data fusion
  • Knowledge discovery
  • Predictive analytics
  • Situation Awareness
  • Threat Assessment

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