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Documents
Title:
Prefetching
For Visual Data Exploration
Authors:
Punit R. Doshi, Elke A. Rundensteiner, Matthew O. Ward, and
Daniel Stroe.
Abstract:
More and more modern computer applications from business decision
support to scientific data analysis utilze visualization techniques
to support exploratory activities for large datasets. Various
tools have been proposed in the past decade that help users better
interpret data using such display techniques. However, such exploratory
visualization tools do not scale well when applied to huge datasets.
Various features provided by database management systems must
be applied to such applications to scale them for huge datasets.
To improve the performance of such visualization systems, caching
the data at client side is necessary. We exploit semantic caching
[24, 8] for the advantages it offers over the traditional enviornments
to prefetch the data for the visualization tools. We have incorporated
these features into XmdvTool [47, 13, 14, 50], a freeware visual
tool for multivariate exploration. We also compare an array of
different prefetching strategies to determine their relative effectiveness
for both synthetic user traces and real users of our system. Our
results show that significant improvement can be achieved for
visualization applications by caching and prefetching the data
on the client-side.
Keywords:
Semantic caching, Prefetching, Large-scale multivariate data visualization,
Exploratory data, analysis, Hierarchical data exploration.
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