Visualizations of large multi-dimensional data sets, occurring in scientific and commercial applications, often reveal interesting local patterns. Analysts want to identify the causes and impacts of these interesting areas, and they also want to search for similar patterns occurring elsewhere in the data set. In this paper we introduce the Intelligent Visual Analytics Query (IVQuery) concept that combines visual interaction with automated analytical methods to support analysts in discovering the special properties and relations of identified patterns. The idea of IVQuery is to interactively select focus areas in the visualization. Then, according to the characteristics of the selected areas, such as the data dimensions and records, IVQuery employs analytical methods to identify the relationships to other portions of the data set. Finally, IVQuery generates visual representations for analysts to view and refine the results. IVQuery has been applied successfully to different real-world data sets, such as data warehouse performance, product sales, and sever performance analysis, and demonstrates the benefits of this technique over traditional filtering and zooming techniques. The visual analytics query technique can be used with many different types of visual representation. In this paper we show how to use IVQuery with parallel coordinates, visual maps, and scatter plots. Visualization often leads to immediate insight in complex phenomena, interesting patterns, or the detection of outliers [2] . In addition to overall patterns and trends, visualizations often reveal interesting local phenomena representing local patterns [3] [4]. Figure 1 , for example, shows local correlations in a database operation monitoring application. Analysts are typically interested in the root-causes of the selected areas. A common way to perform the root-cause analysis is to use interaction techniques, such as filtering and zooming. However, for visual analytics in large high-dimensional data sets, these interaction methods are not sufficient, since in complex data sets a manual search becomes tedious and often impractical. New visual analytics techniques are needed to support the user in analyzing the characteristics, impacts, and relationships of selected global and local patterns. In this paper, we propose the Intelligent Visual Analytics Query (IVQuery) that supports analysts in discovering the properties and relationships of selected patterns. IVQuery follows the Visual Analytics paradigm [5] and aims at integrating analytical and visual methods. The general idea of IVQuery is to let the analyst select an area of interest in generated visualizations (i.e., the green oval in Figure 1 ), identify the attributes (i.e., execution time) and data records that correspond to the selection, and then apply automated analysis methods to identify characteristics of the selected data, as well as their relationships to other attributes and data items. In the example in Figure 1 , IVQuery helps the analyst to quickly monitor database workloads by determining which attributes (i.e., cost, cardinality, etc.) are the best predictors of the execution time. For the latter, state-of-the-art correlation and similarity measures are employed. Depending on the task at hand and the application domain, IVQuery allows the user to plug-in specific correlation and similarity measures, as needed. IVQuery has been successfully applied in a number of different application scenarios, including data warehouse performance analysis, product sales, and server performance analysis, which demonstrate the benefits of this approach over traditional filtering and zooming techniques. IVQuery is a general concept and can be combined with many different types of visualization techniques. In this paper, we show how IVQuery can be integrated with parallel coordinates, scatter plots, and the recently proposed visual map framework [6] . Paper outline: The next section gives an overview of techniques for interactive pattern analysis. Section 2 describes the
Hao et al. REF present IVQuery that combines visual interaction with automated analytics for common data visualization tools.
Intelligent Visual Analytics Queries
2007 IEEE Symposium on Visual Analytics Science and Technology
2007 IEEE Symposium on Visual Analytics Science and Technology
Computer Science