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Proceedings Paper

A survey of visual analytics for knowledge discovery and content analysis
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Paper Abstract

This survey paper provides a review of tools and concepts of visual analytics, and the challenges faced by researchers developing application for knowledge discovery. A comparison is made based on analytic features, its ability to categorize data, the modeling procedures, visual representation, interoperability, and its reliability and portability. The issues related to heterogeneous data, its scalability and multi-dimensionality is also explored. An efficient, intelligent, interactive and robust visual analytics system allows the discovery of information hidden in a massive and dynamic volume of data, especially in a surveillance system thus creating an effective situation awareness of the environment. While visual analytics is hugely important in knowledge discovery, it is necessary for developers to avoid information overload due to inappropriate, irrelevant and uncertain data due to random or fuzzy sensor inputs, also known as noise. The discovered knowledge is the basis for adaptive situation awareness, as it often provides information beyond the perception of human cognitive mind. The tools and concepts researched for this article includes addressing the human computer interaction aspect for intelligent, adaptive decision making from multiple information resources. An attempt is made in this paper to combine the strengths of smart search and data analysis with visual perception and interactive analysis capability of the user.

Paper Details

Date Published: 17 May 2012
PDF: 11 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920T (17 May 2012); doi: 10.1117/12.919311
Show Author Affiliations
Mohammad S. Habibi, Tennessee State Univ. (United States)
Amir Shirkhodaie, Tennessee State Univ. (United States)

Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)

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