Test Data Sets for Evaluating Data Visualization Techniques

In this paper we take a step toward addressing a pressing general problem in the development of data visualization systems — how to measure their effectiveness. The step we take is to define a model for specifying the generation of test data that can be em-ployed for standardized and quantitative testing of a system’s per-formance. These test data sets, in conjunction with appropriate testing procedures, can provide a basis for certifying the effective-ness of a visualization system and for conducting comparative studies to steer system development

Possibilities and Limits in Visualizing Large Amounts of Multidimensional Data

In this paper, we describe our concepts to visualize very large amounts of multidimensional data. Our visualization technique which has been developed to support querying of large scientific databases is designed to visualize as many data items as possible on current display devices. Even if we are able to use each pixel of the display device to visualize one data item, the number of data items that can be visualized is quite limited. Therefore, in our system we introduce reference points (or regions) in multidimensional space and consider only those data items which are 'close' to the reference point. The data items are arranged according to their distance from the reference point. Multiple windows are used for the different dimensions of the data with the distance of each of the dimensions from the reference point (or region) being represented by color. In exploring the database, the reference point (or region) may be changed interactively, allowing different portions of the database to be visualized. To visualize larger portions of the database, sequences of visualizations may be generated automatically by moving the reference point along some path in multidimensional space. Besides describing our visualization technique and several alternatives, we discuss some of the perceptual issues that arise in connection with our visualization technique

From visual data exploration to visual data mining: a survey.

We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension

Applying blended conceptual spaces to variable choice and aesthetics in data visualisation

Computational creativity is an active area of research within the artificial intelligence domain that investigates what aspects of computing can be considered as an analogue to the human creative process. Computers can be programmed to emulate the type of things that the human mind can. Artificial creativity is worthy of study for two reasons. Firstly, it can help in understanding human creativity and secondly it can help with the design of computer programs that appear to be creative. Although the implementation of creativity in computer algorithms is an active field, much of the research fails to specify which of the known theories of creativity it is aligning with. The combination of computational creativity with computer generated visualisations has the potential to produce visualisations that are context sensitive with respect to the data and could solve some of the current automation problems that computers experience. In addition theories of creativity could theoretically compute unusual data combinations, or introducing graphical elements that draw attention to the patterns in the data. More could be learned about the creativity involved as humans go about the task of generating a visualisation. The purpose of this dissertation was to develop a computer program that can automate the generation of a visualisation, for a suitably chosen visualisation type over a small domain of knowledge, using a subset of the computational creativity criteria, in order to try and explore the effects of the introduction of conceptual blending techniques. The problem is that existing computer programs that generate visualisations are lacking the creativity, intuition, background information, and visual perception that enable a human to decide what aspects of the visualisation will expose patterns that are useful to the consumer of the visualisation. The main research question that guided this dissertation was, “How can criteria derived from theories of creativity be used in the generation of visualisations?”. In order to answer this question an analysis was done to determine which creativity theories and artificial intelligence techniques could potentially be used to implement the theories in the context of those relevant to computer generated visualisations. Measurable attributes and criteria that were sufficient for an algorithm that claims to model creativity were explored. The parts of the visualisation pipeline were identified and the aspects of visualisation generation that humans are better at than computers was explored. Themes that emerged in both the computational creativity and the visualisation literature were highlighted. Finally a prototype was built that started to investigate the use of computational creativity methods in the ‘variable choice’, and ‘aesthetics’ stages of the data visualisation pipeline.School of ComputingM. Sc. (Computing

Enhancing comprehension of complex data visualizations: Framework and techniques based on signature exploration

This thesis presents a framework and set of readily applicable techniques for enhancing comprehension of complex data visualizations. Central to the work has been the definition and exploration of a new concept, signature exploration. Visualization is being used increasingly to help make sense of large sets of data and information. Abstractions of complex data can be performed to reduce the dimensions to 2 or 3 for display. Novel or established representations can be used that allow direct mapping of greater numbers of attributes, and of a variety of data structures. There is an ever expanding set of visualization tools available. Two questions face the user: how to choose appropriate displays and how to understand the resultant graphic. This thesis examines how to support the user’s comprehension in this context. The work makes the following three main contributions to enhancing comprehension of complex data visualizations: the definition and application of signature exploration, a concept describing the exploration of visualization behaviour using specially constructed data; the proposal of a framework for the design of visualization systems for increased comprehension; the introduction of two new forms of interaction - which are here described as visual data tracking and feature fingerprinting. The central theme for the exploration presented in this work is the notion that a user wants to take data that is known in some way, put this into the visualization process and assess the resultant visual depiction. This intuitive desire has been captured in the definition of the concept, signature exploration. Signature exploration describes the exploration of the behaviour of visual representations using specially constructed datasets that contain features of interest. The datasets are used to explore the signatures of different visual representations and mathematical transformations. The thesis defines and illustrates signature exploration, with five proposed approaches: generic dataset provision; user-construction of data; querying; insertion of landmarks; elicitation and application of feedback data. These applications of signature exploration, together with analysis of the comprehension challenges presented by different aspects of visualization, and established work to support user comprehension, form the basis of the framework for increased user comprehension. Example software has been developed within the context of a visualization application that employs a number of visualization algorithms to generate graphics for multivariate or proximity data. Principal Components Analysis, Principal Coordinates Analysis and distance metrics of various kinds are the algorithms used. An additional interface is given to the user, to perform signature exploration. The work has resulted in the specification of a set of techniques that developers can readily apply. Two new interaction forms are described: visual data tracking - bi-directional brushing and linking between representations also allowing change of position or value; feature fingerprinting - synthetic additions to real-world datasets to provide the user with calibration of the visual depiction

Test Data Sets for Evaluating Data Visualization Techniques

In this paper we take a step toward addressing a pressing general problem in the development of data visualization systems --- how to measure their effectiveness. The step we take is to define a model for specifying the generation of test data that can be employed for standardized and quantitative testing of a system's performance. These test data sets, in conjunction with appropriate testing procedures, can provide a basis for certifying the effectiveness of a visualization system and for conducting comparative studies to steer system development. Keywords: Testing Data Visualizations, Generating Test Data, Visualizing Multidimensional and Multivariate Data, Perception of Visualizations to appear in: `Perceptual Issues in Visualization', Springer, 1994. - 2 - 1 Introduction Data visualization has captured very high interest among scientists and many commercial and public domain visualization systems have appeared in recent years including, for example, AVS [Ups 89], IBM's Data..