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Novel Techniques for Visualizing Multi-dimensional Data in XmdvTool

This research extends XmdvTool to enable exploring large-scale datasets with hundreds of dimensions and nominal fields, developing methods for ordering, spacing, and clustering dimensions and values. The goal is to create effective tools for visual exploration of high-dimensional and heterogeneous data, fostering intuitive interaction and multi-resolution visualization. The research aims to enhance data exploration capabilities across various disciplines by providing analysts with tools to study larger and diverse datasets.

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Novel Techniques for Visualizing Multi-dimensional Data in XmdvTool

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  1. Grant Number: IIS- 0119276 Institution of PI: WPIPIs: Matthew O. Ward and Elke A. RundensteinerTitle: Order, Spacing, and Clustering in Visual Exploration of Large Scale Data XmdvTool, a public-domain exploratory visualization system, has been extended to enable multi-resolution processing of data sets with nominal fields and with hundreds of dimensions. Novel methods for exploration of relationships between possibly a high number of dimensions facilitates this process. The goal of this research is the development of novel and effective techniques for the exploratory visualization of data of high dimensionality and heterogeneous data types. The approach consists of developing techniques and tools to effectively order, space, and cluster both dimensions as well as values within nominal variables. Our techniques enable the users to visualize the resulting information in a multi-resolution fashion, to interact with the information spaces in an intuitive fashion, and to access the data at a rate to support the exploration process. The research will result in tools that enable analysts to study and explore data sets of a much larger scale and diversity than currently possible with existing technology. A wide range of disciplines will be positively affected, including sciences, engineering, and commerce.

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