Overview of visual analytics

Visual analytics is science of analytical reasoning facilitated by visual interactive interfaces. It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition. As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Visual Analytics is a relatively new multidisciplinary field that combines various research areas including knowledge discovery, data analysis, visualization, human-computer interaction, data management, geo-spatial and temporal data processing and statistics. Visual Analytics has seen unprecedented growth in the past five years and is expected to grow ten folds in the coming future seeing the amount of massive, dynamic and heterogeneous data which concludes nothing unless put to use using the visual analytical techniques. The basic goal of visual analytics includes deriving an insight from the large amount of dynamic data which is from different sources and to analyze them to discover the unexpected and provide timely, defensible, and understandable assessments which can be then communicated effectively for action. An integration of the increasing processing power of computers with the efficient pattern recognition abilities and domain knowledge of human analysts is a challenging and promising road in dealing with large amounts of complex data. It will be also a major driving force for solutions for information overload in many research and commercial areas.