Treffer: Review of information visualization.

Information visualization is the application of visualization technology in non-spatial data area, enhancing data presentation effect. Users can observe the data intuitively and interactively so as to find implicit features, relations and patterns in data. The application of information visualization is very abroad which includes data mining visualization, network data visualization, social data visualization, traffic visualization, text visualization, and medicine visualization, etc. According to Card model on information visualization, process of information visualization includes three stages: data pretreating, data plotting, data displaying and interacting. Ben Shneiderman notes that visualization data includes one-dimensional data, two-dimensional data, three-dimensional data, multi-dimensional data, temporal data, hierarchical data, and network data, of which are given much attention to research. Visualization methods of multi-dimensional data include geometry methods, icon methods, and animation methods, etc. Among the geometry-based visualization methods, the most classic one is the parallel coordinates approach. It uses parallel vertical axis to represent the dimension values. By the multidimensional data portrayed on the shaft, and by the coordinate point connected with a line to a data entry on the axes, the multidimensional data was presented. Multi-dimensional data was displayed concisely and quickly in Parallel Coordinates, and improved many techniques. When scale of data set was very large, the dense lines could cause visual clutter. The methods of clutter reduction include dimension reordering, interacting, clustering and filtering, and visual enhancement, etc. Other methods based on geometry, including Radviz (Radial Coordinate visualization), display multi-dimensional data by circular coordinate. Scatter plot matrix arranges every demensions of multidimensional data to be combined into pairwise mode, drawing a series of regular scatters. Icon was used to describe the multi-dimensional data by its geometrical features including size, length, form and color, etc. Icon methods include star graph and Chernoff face method. Animation used for visualization can improve the degree of interacting and understanding., but with shortcomings such as: distraction, misunderstanding and visual clutter. Time serial data refers to data sets with time property. The visualization methods include line chart, stock chart, animation, horizon graph and Timeline. Hierarchical data can be used to describe object whose attributes are rank and level. Its visualization methods include linking point graph and tree map. Tree map displays hierarchical data by nesting hoop and lump. For displaying more content, based on "Focus+Content" technology, some methods were put forward including "fish eyes" technology, geometry deformation, Semantic zooming and clustering. Network data has network structure. Layout algorithm is the core of visualization of network data, which includes three classes: Force-Directed Layout, Hierarchical Layout and Grid Layout. When there're many data connection nodes, edge corssover phenomenon happens, causing visual confusion. There were a variety of techniques for resolving the edge bundling, including hierarchical edge bundling, force-directed edge bundling, geometry-based edge clustering, multi-level agglomerative edge bundling, and grid-based methods. Other research hotspots include research on visual feature, adaptive visualization and evaluation of information visualization. Effect of visual feature such as position, length, area, shape and color, etc. on visual result has received considerable attention. Color is one of most important visual factor, so research focuses on the color selection principle and interaction system, which are based on data type, quantity, and cognitive constraints. Adaptive visualization can enhance adaptability of information visualization, which includes adaptive display, adaptive resource model, and adaptive user model according to research of Domik & Gutkauf and Grawemeyer & Cox. Adaptive display provides automatic and suitable display for different users, including selecting content and layout, adjusting visual features automatically. Adaptive resource model means utilizing hardware and software to enhance visual performance. Adaptive user model means displaying user model in order to edit and control content. Morse et al. notes that the research on evaluation of information visualizations is rare. Evaluation on direct and general information visualization was not involved in some research. So, it is needed to do deep research on the theoretical basis, method and application of information visualization evaluation. Technology and application of information visualization should be developed in four aspects, displaying data directly perceived through the senses, mining and showing relation between data, strengthening demonstration of aesthetics and artistry, enhancing performance of interaction and operation on real-time data. Dai et al. noted that research direction of information visualization was Collaboration, Analytics, Computational and Sense-making. Research directions in future is as following. Visualization and data mining: to promote efficiency and avoid visual clutter in processing huge data, information visualization should be combined with data mining so that user can operate huge data and discover implicit information. Collaborative visualization: Collaborative visualization includes interface design, collaborative platform based on web, view design, workflow design, and application of technology. Application in more fields: statistics visualization refers to processing and handling the statistical process data and results by method of geometry, animation, and graph ett. News visualization refers to presenting diversely analysis results after grasping, cleaning, and drawing news corpus. Social network visualization refers to displaying and revealing relation, comparison, and trend of social network through integration of dimensions of time and space. Search log visualization refers to displaying huge searching behavior when using a search engine. Users' search behavior, relationships and patterns are presented visually. [ABSTRACT FROM AUTHOR]

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