Visualization Search Strategies

Abstract

Innovations in high performance computing and high bandwidth networks have led to the onset of data explosion. Along with large size, the datasets are typically multivariate. The need for effective exploration of this data has led to the area of multidimensional visualization. Research in low level human visual system has resulted in the construction of perceptual guidelines that can produce effective visualizations. However, application of these guidelines to a dataset requires users to be experts in the visualization domain.

ViA is a semi-automated visualization assistant that uses perceptual guidelines along with a heuristic search algorithm to generate perceptually salient visualizations.

This thesis aims to study the behavior of the current hint-based search strategy and determine its efficiency. We compare hint-based search with two generic heuristic search algorithms, simulated annealing and reactive tabu search, by adapting them to ViA's search domain. We use time efficiency, space efficiency, ability to find multiple optimal solutions and optimality as performance metrics. Further, in order to "see" the areas of the search space explored by each search algorithm, we have developed a focus + context visualization system using hyperbolic geometry.