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Polaris: A System for Query, Analysis, and Visualization of Multidimensional Relational Databases Chris Stolte, Diane Tang, and Pat Hanrahan Abstract ÐIn the last several years, large multidimensional databases have become common in a variety of applications such as data warehousing and scientific computing. Analysis and exploration tasks place significant demands on the interfaces to these databases. Because of the size of the data sets, dense graphical representations are more effective for exploration than spreadsheets and charts. Furthermore, because of the exploratory nature of the analysis, it must be possible for the analysts to change visualizations rapidly as they pursue a cycle involving first hypothesis and then experimentation. In this paper, we present Polaris, an interface for exploring large multidimensional databases that extends the well-known Pivot Table interface. The novel features of Polaris include an interface for constructing visual specifications of table-based graphical displays and the ability to generate a precise set of relational queries from the visual specifications. The visual specifications can be rapidly and incrementally developed, giving the analyst visual feedback as they construct complex queries and visualizations. Index Terms ÐDatabase visualization, database analysis, visualization formalism, multidimensional databases. æ 1I NTRODUCTION I N the last several years, large databases have become common in a variety of applications. Corporations are creating large data warehouses of historical data on key aspects of their operations. International research pro- jects such as the Human Genome Project [20] and Digital Sky Survey [31] are generating massive data- bases of scientific data. A major challenge with these databases is to extract meaning from the data they contain: to discover structure, find patterns, and derive causal relationships. The analysis and exploration necessary to uncover this hidden informa- tion places significant demands on the human-computer interfaces to these databases. The exploratory analysis process is one of hypothesis, experiment, and discovery. The path of exploration is unpredictable and the analysts need to be able to rapidly change both what data they are viewing and how they are viewing that data. The current trend is to treat multidimensional databases as n-dimensional data cubes [16]. Each dimension in these data cubes corresponds to one dimension in the relational schema. Perhaps the most popular interface to multi- dimensional databases is the Pivot Table [15]. Pivot Tables allow the data cube to be rotated, or pivoted, so that different dimensions of the dataset may be encoded as rows or columns of the table. The remaining dimensions are aggregated and displayed as numbers in the cells of the table. Cross-tabulations and summaries are then added to the resulting table of numbers. Finally, graphs may be generated from the resulting tables. Visual Insights recently released a new interface for visually exploring projections
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This note was uploaded on 02/13/2012 for the course CS 91.510 taught by Professor Staff during the Fall '09 term at UMass Lowell.

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