Multi-attributed tagged big data exploitation for hidden concepts discovery
Mohammad S. Habibi, Amir Shirkhodaie · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
Analysts who are using visualization methods for big data concept exploration increasingly expect to comprehend more distinct relationships and prominent concepts in support of their hypotheses or decisions. To expedite this knowledge discovery process, Vector Space Modeling (VSM) in conjunction with probabilistic analysis enables rapid knowledgebased relationship discovery while allowing for exploration of multi-embedded concepts than otherwise it is difficult to perceive. In this paper, we present a technique for intrinsic ontology concepts similarity matching based on VSM for exploitation and knowledge discovery from multimodality sensors metadata generated in Persistent Surveillance Systems (PSS). To reduce data dimensionality, Principal Component Analysis (PCA) and Latent Dirichlet Allocation (LDA) is applied to arrive at more abstract concepts. The proposed technique is able to reveal intrinsic concept relationships from multi-dimensional metadata structures. Experimental results demonstrate effectiveness of this approach for analytical ontological patterns exploitation. In this paper, the expediency of this technique for Visual Analytics application is demonstrated. The result indicates that the newly developed system can significantly enhance situation awareness and expedite actionable decision making.