High-dimensional Data in Scientific Visualization: Representation, Fusion and Difference

Mohammed, Ayat Mohammed Naguib

High-dimensional Data in Scientific Visualization: Representation, Fusion and Difference

dc.contributor.author	Mohammed, Ayat Mohammed Naguib	en
dc.contributor.committeechair	Polys, Nicholas F.	en
dc.contributor.committeemember	Interrante, Victoria L.	en
dc.contributor.committeemember	North, Christopher L.	en
dc.contributor.committeemember	Bowman, Douglas A.	en
dc.contributor.committeemember	Hegazy, Doaa Abd Al-Kareem Mohammed	en
dc.contributor.department	Computer Science	en
dc.date.accessioned	2017-07-15T08:00:36Z	en
dc.date.available	2017-07-15T08:00:36Z	en
dc.date.issued	2017-07-14	en
dc.description.abstract	Visualization has proven to be an effective means for analyzing high-dimensional data, especially Multivariate Multidimensional (MVMD) scientific data. Scientific visualization deals with data that have natural spatial mapping such as maps, buildings interiors or even your physiological body parts, while information visualization involves abstract, non-spatial data. Visual analytics uses either visualization types to gain deep inferences about scientific data or information. In recent years, a variety of techniques have been developed combining statistical and visual analysis tools to represent data of different types in one view to enable data fusion. One vital feature of such visualization tools is the support for comparison: showing the differences between two or more objects. This feature is called visual differencing, or discrimination. Visual differencing is a common requirement across different research domains, helping analysts compare different objects in the data set or compare different attributes of the same object. From a visual analytic point of view, this research examines humans' predictable bias in interpreting visual-spatial, spatiotemporal information, and inference-making in scientific visualization. Practically, I examined different case studies from different domains such as land suitability in agriculture, spectrum sensing in software-defined radio networks, raster images in remote sensing, pattern recognition in point cloud, airflow distribution in aerodynamics, galaxy catalogs in astrophysics and protein membrane interaction in molecular dynamics. Each case required different computing power, ranging from personal computer to high performance cluster. Based on this experience across application domains, I propose a high-performance visualization paradigm for scientific visualization that supports three key features of scientific data analysis: representations, fusion, and visual discrimination. This paradigm is informed by practical work with multiple high-performance computing and visualization platforms from desktop displays to immersive CAVE displays. In order to evaluate the applicability of the proposed paradigm, I carried out two user studies. The first user study addressed the feature of data fusion with multivariate maps and the second one addressed visual differencing with three multi-view management techniques. The high-performance visualization paradigm and the results of these studies contribute to our knowledge of efficient MVMD designs and provides scientific visualization developers with a framework to mitigate the trade-offs of scalable visualization design such as the data mappings, computing power, and output modality.	en
dc.description.abstractgeneral	Visualization has proven to be an effective means for analyzing big data such as Multivariate Multidimensional (MVMD) scientific data. Scientific visualization deals with data that have natural spatial mapping such as maps, buildings interiors or even your physiological body parts, while information visualization involves abstract, non-spatial data. Visual analytics uses visualization to interactively manipulate data to gain deep inferences about scientific data or information. A variety of techniques combining statistical and visual analysis tools have been developed in the recent years, one of the most interesting techniques is Information Rich Virtual environments (IRVEs). With visual differencing, discrimination or interpretation as a vital feature addresses its functionality of showing the differences between two or more objects when comparison is needed. Visual differencing is widely needed across different research domains to help analysts identifying different objects in the data set or identifying different attributes of the same object. From a visual analytics point of view, this research is examining humans predictable bias in interpreting visual-spatial(1D, 2D, and 3D data) and spatiotemporal (datasets have space and time dimensions) information and inference making in scientific visualization. Also this research seeks to develop and evaluate new techniques to mitigate the trade-off between proximity and occlusion in the visualization scenes and enable analysts to explore high-dimensional scientific data sets. This research is seeking powerful computational techniques combined with natural human interactions and visual communication to analyze scientific data. This research proposes a high-performance visualization paradigm for scientific visualization that supports different representations of scientific data, fusion of different types of data, and visual discrimination that enables users to visually find the difference between multiple objects in the visualization scene. I examined different case studies from different domains such as land suitability in agriculture, spectrum sensing in software-defined radio networks, raster images in remote sensing, pattern recognition in point cloud, airflow distribution in aerodynamics, galaxy catalogs in astrophysics and protein membrane interaction in molecular dynamics. Each case required different computing power, ranging from personal computer to high-performance cluster. Also, different rendering venues were needed, starting with desktop displays to the immersive CAVE displays.	en
dc.description.degree	Ph. D.	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:11752	en
dc.identifier.uri	http://hdl.handle.net/10919/78343	en
dc.language.iso	en	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Multivariate Multidimensional Data (MVMD)	en
dc.subject	Scientific Visualization	en
dc.subject	Visual Discrimination	en
dc.subject	perception	en
dc.subject	High-performance visualization Paradigm	en
dc.title	High-dimensional Data in Scientific Visualization: Representation, Fusion and Difference	en
dc.type	Dissertation	en
thesis.degree.discipline	Computer Science and Applications	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Ph. D.	en