Abstract
Modern computer applications, from business decision support to scientific data analysis, utilize visualization techniques to support exploratory activities. However, most existing visual exploration tools do not scale well for large data sets, i.e., the level of cluttering on the screen is typically unacceptable and the performance is poor. To solve the cluttered interface problem, visualization tools have recently been extended to support hierarchical views of the data, with support for focusing and drilling-down using interactive brushes. To solve the scalability problem, we now investigate how best to couple such a near real-time responsive visualization tool with a database management system. This integration must be done carefully, since the direct implementation of the visual user interactions on hierarchical datasets corresponds to recursive query processing and thus is highly inefficient. For this problem, we have developed a tree labeling method, called MinMax tree, that allows the movement of the on-line recursive processing into an off-line precomputation step. Thus at run time, the recursive processing operations translate into linear cost range queries. Secondly, we employ a main memory access strategy to support incremental loading of data into the main memory. The techniques have been incorporated into XmdvTool, a visual exploration tool, to achieve scalability. Lastly, we report experimental results that illustrate the impact of the proposed techniques on the system’s overall performance.
This work is supported under NSF grant IIS-9732897 and NSF CISE Instrumentation grant IRIS 97-29878.
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Stroe, I.D., Rundensteiner, E.A., Ward, M.O. (2000). Scalable Visual Hierarchy Exploration. In: Ibrahim, M., Küng, J., Revell, N. (eds) Database and Expert Systems Applications. DEXA 2000. Lecture Notes in Computer Science, vol 1873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44469-6_73
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DOI: https://doi.org/10.1007/3-540-44469-6_73
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