Title page for ETD etd-0510100-142928

Document Type thesis

Author Name Stroe, Ionel Daniel

URN etd-0510100-142928

Title Scalable Visual Hierarchy Exploration

Degree MS

Department Computer Science

Advisors
Elke A. Rundensteiner, Advisor
Matthew O. Ward, Advisor
Carolina Ruiz, Reader

Keywords
semantic caching
prefetching
recursive queries
hierarchical structures
database backend
visual exploration

Date of Presentation/Defense 2000-04-28

Availability unrestricted

Abstract
More and more modern computer applications, from business decision support to scientific data analysis, utilize visualization techniques to support exploratory activities. Various tools have been proposed in the past decade to help users better interpret data using such display techniques. However, most do not scale well with regard to the size of the dataset upon which they operate. In particular, the level of cluttering on the screen is typically unacceptable and the performance is poor. To solve the problem of cluttering at the interface level, 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 visualization tool with a database management system without losing the real-time characteristics. This integration must be done carefully, since visual user interactions implemented as main memory operations do not map directly into efficient database operations.
The main efficiency issue when doing this integration is to avoid the recursive processing required for hierarchical data retrieval. For this problem, we have develop 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 multidimensional visual exploration tool, in order to achieve scalability. The tool now successfully scales up to datasets of the order 10^5-10^7 records. Lastly, we report experimental results that illustrate the impact of the proposed techniques on the system's overall performance.

Files
stroe.pdf

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Document Type	thesis
Author Name	Stroe, Ionel Daniel
URN	etd-0510100-142928
Title	Scalable Visual Hierarchy Exploration
Degree	MS
Department	Computer Science
Advisors	Elke A. Rundensteiner, Advisor Matthew O. Ward, Advisor Carolina Ruiz, Reader
Keywords	semantic caching prefetching recursive queries hierarchical structures database backend visual exploration
Date of Presentation/Defense	2000-04-28
Availability	unrestricted
Abstract More and more modern computer applications, from business decision support to scientific data analysis, utilize visualization techniques to support exploratory activities. Various tools have been proposed in the past decade to help users better interpret data using such display techniques. However, most do not scale well with regard to the size of the dataset upon which they operate. In particular, the level of cluttering on the screen is typically unacceptable and the performance is poor. To solve the problem of cluttering at the interface level, 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 visualization tool with a database management system without losing the real-time characteristics. This integration must be done carefully, since visual user interactions implemented as main memory operations do not map directly into efficient database operations. The main efficiency issue when doing this integration is to avoid the recursive processing required for hierarchical data retrieval. For this problem, we have develop 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 multidimensional visual exploration tool, in order to achieve scalability. The tool now successfully scales up to datasets of the order 10^5-10^7 records. Lastly, we report experimental results that illustrate the impact of the proposed techniques on the system's overall performance.
Files	stroe.pdf