Matthew Ward's Thesis Project Topics

  1. Visual Data Mining: Data mining involves exploring databases to try and discover data relationships which are not explicitly stored within the databases. Traditional techniques involve statistical analysis, clustering, and pattern matching. Many current efforts are underway to integrate visualization into this process. This project involves examining the benefits that would result from using multivariate data visualization in conjunction with analytic methods to explore databases.
  2. Perceptual Benchmarking: There have been a large number of techniques developed over time for the display of multivariate data, yet little work has been done on evaluating the relative effectiveness of each technique. It is conjectured that the usefulness of each method depends both on the characteristics of the data (size, number of parameters) and the perception task at hand detect/classify, patterns/clusters/anomalies). This project involves the development of benchmarks for comparing the effectiveness of visualization techniques and running experiments with human subjects to assess the quality of the benchmarks.
  3. Extensions to XmdvTool: XmdvTool is a package developed by myself and Allen Martin (MS '95) for the visualization and exploration of multivariate data using a variety of projection and interaction methods. Additional work on this tool can focus on a number of areas, including the integration of animation, specialized tools for each of the projection methods (similar to work done by Jeff LeBlanc MS '91 and Rajeev Tipnis MS '92), tailoring visualizations based on data semantics, an intelligent data configuration front-end, and customizable "smart" probes. A current NSF grant is focused on extending the visualization techniques in XmdvTool to handle very large, hierarchical data sets.
  4. Extensions to MAVIS: MAVIS is a program written by Chris Bentley (MS '96) which uses a statistical technique known as Multidimensional Scaling (MDS) to display multivariate data. MDS is an iterative refinement method for positioning n-dimensional data into a lower dimensional space, and MAVIS animates this process in 1-D, 2-D, or 3-D. It supports numerous ways of visualizing the evolution over time via animation and flow visualization, and provides numerous ways of controlling the process. Additional work on this tool include incorporating and comparing other dimensional reduction techniques, add a clustering capability to allow hierarchical processing, and experimenting with other flow visualization methods.
  5. Visualizing Nominal Data: Most visualization techniques cater to ordinal data, i.e. data with values that have an order associated with them. This is because most graphical attributes are ordinal in nature (e.g. size, position, intensity). However, a great deal of data is of a nominal type, such as categorical data. The problem with visualizing this in traditional ways is that the ordinal nature of the graphical attribute may introduce errors in the interpretation of the visualization. This project will focus on exploring methods for visually presenting nominal data which minimize or eliminate the distortion or error introduced by the graphical mapping/perception process.
  6. Extensions to XSauci: XSauci is a package developed by myself, Dave Nedde (MS '91), and Maureen Higgins (MS BB '92) for the display of information regarding genetic sequences (John Rasku, MS '93 used similar methods for analyzing shapes). Techniques supported include correlation images (which plot a matrix showing matching seqence elements) and density/distribution charts. Additional work on this tool can focus on integrating more elaborate matching algorithms (which can deal with fuzzy matching, substitutions, and gaps), tying in some quantitative analysis tools (e.g. dynamic programming, statistical methods), and enhancing the visual presentation of the data.

Matthew O. Ward (