Techniques for Accelerating Aggregated Range Queries on Large Multidimensional Datasets in Interactive Visual Exploration

Visual exploration of large multidimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real-time. The fundamental insight of these techniques is that the performance of interactive visual data exploration systems can be improved by accelerating aggregated range queries. However, the extant state techniques still have limitations, such as low expressivity and large memory footprint. In this dissertation, I present three techniques, GaussianCubes, NeuralCubes, and TopoCubes, each tackling different problems existing techniques can not solve. GaussianCubes significantly improves on datacube-based systems by providing interactive modeling capabilities, which include but are not limited to linear least squares and principal components analysis. NeuralCubes leverage the recent advancement in deep neural networks to learn a model that takes as input a given query and outputs the approximated result. The learned model serves as a real-time, low-memory approximator for aggregation queries. The model is small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling subsequent exploration of large datasets without database/network connection. Finally, TopoCubes extends existing preaggregation techniques to improve the efficiency of compute-intensive tasks, such as Topological Data Analysis algorithms, again, in an interactive data analysis system.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Graduate College

Grantor dc:publisher

The University of Arizona.

Year dc:date.issued

2019

Author dc:creator

• Wang, Zhe

Advisor dc:contributor.advisor

• Scheidegger, Carlos

Committee members dc:contributor.committeemember

• Levine, Joshua A.
• Snodgrass, Richard T.
• Chang, Remco

• data management
• data visualization
• deep learning
• interactive data analysis

Statement dc:rights

• Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10150/634304

OAI identifier oai:identifier

oai:repository.arizona.edu:10150/634304