Shenghui Cheng

Type

Text

Type

Dissertation

Advisor

Advisor: Mueller, Klaus | Committee members: Gu, Xianfeng; Kaufman, Arie; Yan, Hanfei

Date

2018

Keywords

Multivariate Data, Visual Analytics

Department

Department of Computer Science.

Language

en

Source

This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.

Identifier

http://hdl.handle.net/11401/78501

Publisher

The Graduate School, Stony Brook University: Stony Brook, NY.

Format

application/pdf

Abstract

The growth of digital data is tremendous. These data come from many aspects of life and matter such as medicine, science, environment monitoring, business, finance, social networks, etc. When the data is multivariate, or the dimensionality of the data becomes high, it can be a challenge for analysts to understand the intricate relations among the data. The data types not only consist of static data, but also dynamic data, geospatial data, network data etc. The various types make it even more difficult for the analysis. Visual analytics can offer powerful mechanisms to assist humans in the exploration of these complex data, by mining the relations from the raw data and sculpting them as visualizations to help humans gain insight. In the thesis, we focus on relation discovery in multivariate static, dynamic, geospatial, and network data via several new visual analytics approaches. First, we analyze the relations among the static multivariate data and propose the data context map which can illustrate the relations among data items and attributes. Then we extend the mapping to the dynamic case, aiming to capture and visualize the attribute relation behaviors in dynamic flows with our tool StreamVisND. Next, we move to the geospatial data to recover the relations in the geospatial data. To achieve this, we developed the ColorMapND framework to visualize and colorize multi-field, multi-channel, multi-spectral data on the geospatial or image domain. Finally, we consider the complex topology that shapes the multivariate data, such as network data and visualize the relations in this kind of complex network topology. We first study the relations of common networks by modified spectral embedding and then extend our work to multi-dimensional torus networks with the proposed framework TorusTra f f icND. 234 pages

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