Interactive Visual Exploration of Large Relational Data Download

Author: Mustafa Sanver
Edition:
Binding: Paperback
ISBN: 3639132319

Interactive Visual Exploration of Large Relational Data: Using multiresolution data aggregations built on a high-dimensional index

Visualization is a powerful tool to capture profound insights. Get Interactive Visual Exploration of Large Relational Data computer books for free.
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 Check Interactive Visual Exploration of Large Relational Data our best computer books for 2013. All books are available in pdf format and downloadable from rapidshare, 4shared, and mediafire.

Interactive Visual Exploration of Large Relational Data Download

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 owever, 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

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