Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 166-175). Also available on the World Wide Web.

Thesis (M.S.)--University of Central Florida, 2009. / Adviser: Joseph J. LaViola Jr. Includes bibliographical references (p. 79-84).

Thesis (M.S.)--University of Missouri-Columbia, 2008. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 12, 2009) Includes bibliographical references.

Habilitation - Universitat, Stuttgart. / Title from e-book title screen (viewed January 2, 2008). Includes bibliographical references (p. [249]-275) and index.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xiv, 126 p.; also includes graphics. Includes bibliographical references (p. 119-126). Available online via OhioLINK's ETD Center

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

The emergence of tools that support fast and easy creation of visualizations has made the benefits of Information Visualization (InfoVis) more accessible. The predominant design for visualization authoring tools often includes features such as automated mappings and visualization templates, which make tools effective and easy-to-use. These features, however, still impose barriers to non-experts (i.e., people with no formal training on visualization concepts). The paradigm of Constructive Visualization (ConstructiveVis) has shown potential to overcome some of these barriers, but it has only been investigated through the use of physical tokens that people manipulate to create representations of data. This dissertation investigates how the principles of ConstructiveVis can be applied in the design and implementation of digital constructive visualization tools. This thesis presents the results of several observational studies that uncover how tools that promote a constructive approach to visualization compare to more conventional ones. It also sheds light on what kind of benefits and limitations digital ConstructiveVis brings into non-experts' visualization design process. The investigations here presented lay the foundations for the design of better visualization tools that not only allow people to create effective visualizations but also promote critical reflection on design principles.

No description available.

In decision theory the process of selecting the best option is called preferential choice. Many personal, business, and professional preferential choice decisions are made every day. In these situations, a decision maker must select the optimal option among multiple alternatives. In order to do this, she must be able to analyze a model of her preferences with respect to the objectives that are important to her. Prescriptive decision theory suggests several ways to effectively develop a decision model. However, these methods often end up too tedious and complicated to apply to complex decisions that involve many objectives and alternatives. In order to help people make better decisions, an easier, more intuitive way to develop interactive models for analysis of decision contexts is needed. The application of interactive visualization techniques to this problem is an opportune solution. A visualization tool to help in preferential choice must take into account important aspects from both fields of Information Visualization and Decision Theory. There exists some proposals that claim to aid preferential choice, but some key tasks and steps from at least one of these areas are often overlooked. An added missing element in these proposals is an adequate user evaluation. In fact, the concept of a good evaluation in the field of information visualization is a topic of debate, since the goals of such systems stretch beyond what can be concluded from traditional usability testing. In our research we investigate ways to overcome some of the challenges faced in the design and evaluation of visualization systems for preferential choice. In previous work, Carenini and Lloyd proposed ValueCharts, a set of visualizations and interactive techniques to support the inspection of linear models of preferences. We now identify the need to consider the decision process in its entirety, and to redesign ValueCharts in order to support all phases of preferential choice. We present our task-based approach to the redesign of ValueCharts grounded in recent findings from both Decision Analysis and Information Visualization. We propose a set of domain-independent tasks for the design and evaluation of interactive visualizations for preferential choice. We then use the resulting framework as a basis for an analytical evaluation of our tool and alternative approaches. Finally, we use an application of the task model in conjunction with a new blend of evaluation methods to assess the utility of ValueCharts. / Science, Faculty of / Computer Science, Department of / Graduate

There have been a number of technological advances in the last ten years, which has resulted in the amount of data generated in organisations increasing by more than 200% during this period. This rapid increase in data means that if financial institutions are to derive significant value from this data, they need to identify new ways to analyse this data effectively. Due to the considerable size of the data, financial institutions also need to consider how to effectively visualise the data. Traditional tools such as relational database management systems have problems processing large amounts of data due to memory constraints, latency issues and the presence of both structured and unstructured data The aim of this research was to use data analysis and information visualisation techniques (IV) to support the effective analysis of large financial data sets. In order to visually analyse the data effectively, the underlying data model must produce results that are reliable. A large financial data set was identified, and used to demonstrate that IV techniques can be used to support the effective analysis of large financial data sets. A review of the literature on large financial data sets, visual analytics, existing data management and data visualisation tools identified the shortcomings of existing tools. This resulted in the determination of the requirements for the data management tool, and the IV tool. The data management tool identified was a data warehouse and the IV toolkit identified was Tableau. The IV techniques identified included the Overview, Dashboards and Colour Blending. The IV tool was implemented and published online and can be accessed through a web browser interface. The data warehouse and the IV tool were evaluated to determine their accuracy and effectiveness in supporting the effective analysis of the large financial data set. The experiment used to evaluate the data warehouse yielded positive results, showing that only about 4% of the records had incorrect data. The results of the user study were positive and no major usability issues were identified. The participants found the IV techniques effective for analysing the large financial data set.