Interactive visual analysis

Interactive Visual Analysis (IVA) is a set of techniques for combining the computational power of computers with the perceptive and cognitive capabilities of humans, in order to extract knowledge from large and complex datasets. The techniques rely heavily on user interaction and the human visual system, and exist in the intersection between visual analytics and big data. It is a branch of data visualization. IVA is a suitable technique for analyzing high-dimensional data that has a large number of data points, where simple graphing and non-interactive techniques give an insufficient understanding of the information.

These techniques involve looking at datasets through different, correlated views and iteratively selecting and examining features the user finds interesting. The objective of IVA is to gain knowledge which is not readily apparent from a dataset, typically in tabular form. This can involve generating, testing or verifying hypotheses, or simply exploring the dataset to look for correlations between different variables.

Focus + Context visualization and its related techniques date back to the 1970s. Early attempts at combining these techniques for Interactive Visual Analysis occur in the WEAVE visualization system for cardiac simulation in the year 2000. SimVis appeared in 2003, and multiple Ph. D. projects have explored the concept since then - notably Helmut Doleisch in 2004, Johannes Kehrer in 2011 and Zoltan Konyha in 2013. ComVis, which is used in the visualization community, appeared in 2008.

The objective of Interactive Visual Analysis is to discover information in data which is not readily apparent. The goal is to move from the data itself to the information contained in the data, ultimately uncovering knowledge which was not apparent from looking at the raw numbers.

The most basic form of IVA is to use coordinated multiple views displaying different columns of our dataset. At least two views are required for IVA. The views are usually among the common tools of information visualization, such as histograms, scatterplots or parallel coordinates, but using volume rendered views is also possible if this is appropriate for the data. Typically, one view will display the independent variables of the dataset (e.g. time or spatial location), while the others display the dependent variables (e.g. temperature, pressure or population density) in relation to each other. If the views are linked, the user can select data points in one view and have the corresponding data points automatically highlighted in the other views. This technique, which intuitively allows exploration of higher-dimensional properties of the data, is known as linking and brushing.

...
Wikipedia