Interactive Visual Exploration of Large Relational Data
Using multiresolution data aggregations built on a high-dimensional index
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Visualization is a powerful tool to capture profound insights. However, interactive visual exploration of massive data in many activities from business decision making to scientific analysis poses fundamental technical challenges to both data visualization and database management systems. In this work, we propose a density-based methodology to address the challenge. We present multiresolution data aggregation as an intermediate representation ofdata between visualization tools and databases. Data aggregated at multiple resolutions are stored in internal nodes of a partition-based high dimensio...
Visualization is a powerful tool to capture profound
insights. However, interactive visual exploration of
massive data in many activities from business
decision making to scientific analysis poses
fundamental technical challenges to both data
visualization and database management systems.
In this work, we propose a density-based methodology
to address the challenge. We present multiresolution
data aggregation as an intermediate representation of
data between visualization tools and databases. Data
aggregated at multiple resolutions are stored in
internal nodes of a partition-based high dimensional
tree index while the individual records are stored
in leaf nodes. Such a piggyback ride of aggregated
data efficiently supports resolution-based data
access patterns of large relational data.
Multiresolution data aggregation provides density-
based data input to visualization techniques. A
software tool (mVis) is developed to
demonstrate the feasibility and effectiveness of the
new input. It is a dynamic, flexible, and extendible
framework which currently constitutes several
visualization techniques that support the new data
representation.
insights. However, interactive visual exploration of
massive data in many activities from business
decision making to scientific analysis poses
fundamental technical challenges to both data
visualization and database management systems.
In this work, we propose a density-based methodology
to address the challenge. We present multiresolution
data aggregation as an intermediate representation of
data between visualization tools and databases. Data
aggregated at multiple resolutions are stored in
internal nodes of a partition-based high dimensional
tree index while the individual records are stored
in leaf nodes. Such a piggyback ride of aggregated
data efficiently supports resolution-based data
access patterns of large relational data.
Multiresolution data aggregation provides density-
based data input to visualization techniques. A
software tool (mVis) is developed to
demonstrate the feasibility and effectiveness of the
new input. It is a dynamic, flexible, and extendible
framework which currently constitutes several
visualization techniques that support the new data
representation.
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