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.

A visualização computacional de informação é um campo em expansão por oferecer meios de se interpretar e analisar vários tipos de dados em grande quantidade e/ou de grande complexidade, compreendendo diversas técnicas e ferramentas para fornecer a um usuário formas de interagir e explorar conjuntos de dados a fim de se obter informações úteis ou importantes. A música, por sua vez, é um domínio complexo e de difícil estudo sob o ponto de vista computacional devido à análise de seu conteúdo possuir caráter muitas vezes subjetivo e dependente da interpretação humana. Embora vários trabalhos tenham sido publicados a respeito do assunto nos últimos anos, a maior parte das aplicações de visualização de informação relativas a música tende a analisar conjuntos de composições musicais a fim de agrupar ou classificar dados de acordo com algum tipo de critério. Assim, a visualização das informações contidas em uma única peça musical por si só é uma área que ainda pode ser melhor explorada, sobretudo visando compreender a informação musical envolvida o conteúdo extraído por um músico a partir de partituras e tablaturas. Esta dissertação relata o desenvolvimento de uma abordagem para visualização de dados musicais referentes a melodias em guitarra, com a capacidade de exibir elementos como variações de harmonia, melodia e tempo, tendo como objetivo auxiliar um músico (ou aprendiz de músico) na tarefa de interpretar tais dados. / Information visualization is an expanding research field due to its offering of novel approaches to analyze data of great size or complexity, referring to many techniques and tools in order to offer ways to interact and explore data sets to find important or useful information. Music is a domain of high complexity and hard to study and analyze by computer due to its sometimes subjective features, dependant of human interpretation. Although many research initiatives have been published regarding this subject recently, most of the music-related information visualization applications tend to analyze datasets composed by many different musical pieces, aiming to classify or group the data according to certain criteria. Thus, visualization of the information contained in a single musical piece is an area that still could be better explored, especially regarding to the comprehension of the musical information involved information extracted by a musician by reading musical scores. This document reports the development of a novel approach to musical data visualization based on electric guitar melodies, capable of showing elements such as harmony, melody and timing variations, aiming to aid a musician in the task of understanding such data.

Design form choices and information visualization outcomes remain inexhaustible, and they result from ongoing judgments about their appropriateness or effectiveness. Visualization design decision models have been widely proposed and applied. However, experts fail to explicitly study using design judgment to produce informed, professional decisions. In this dissertation, I bridge design form informational judgment gap when analyzing five studies with lab and in-situ designs individually as well as cross-case synthetically and comparably to examine the design judgments of all students working with visualization projects. The outcome stands to explain comprehensively how these student designers make judgments throughout their design process. Through analyzing several design cases, I identify the judgments enabling design moves forward and outcomes. The findings provide a robust description of designers’ design judgment activities and how the design judgment methods relate to design outcomes. These findings may also help identify gaps in information visualization education.<br>

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Across domains, researchers, engineers, and designers are faced with large volumes of data that are heterogeneous in nature - including spatial, abstract, and temporal information. There are numerous design and technical challenges when considering the unification, management, and presentation of these information types. Most research and applications have focused on display techniques for each of the information types individually, but much less in known about how to represent the relationships between information types. This research explores the perceptual and usability impacts of data representations and layout algorithms for the next-generation of integrated information spaces. We propose Information-Rich Virtual Environments (IRVEs) as a solution to challenges of integrated information spaces. In this presentation, we will demonstrate the application requirements and foundational technology of IRVEs and articulate crucial tradeoffs in IRVE information design. We will present a design space and evaluation methodology to explore the usability effects of these tradeoffs. Experimental results will be presented for a series of empirical usability evaluations that increase our understanding of how these tradeoffs can be resolved to improve user performance. Finally, we interpret the results though the models of Information Theory and Human Information Processing to derive new conclusions regarding the role of perceptual cues in determining user performance in IRVEs. These lessons are posed as a set of design guidelines to aid developers of new IRVE interfaces and specifications. / Ph. D.

Geospatial intelligence analysts, epidemiologists, sociologists, and biologists are all faced with trying to understand massive datasets that require integrating spatial and multidimensional data. Information visualizations are often used to aid these scientists, but designing the visualizations is challenging. One aspect of the visualization design space is a choice of when to use a single complex integrated view and when to use multiple simple views. Because of the many tradeoffs involved with this decision it is not always clear which design to use. Additionally, as the cost of display technologies continues to decrease, large, high resolution displays are gradually becoming a more viable option for single users. These large displays offer new opportunities for scaling up visualization to very large datasets. Visualizations that are visually scalable are able to effectively display large datasets in terms of both graphical scalability (the number of pixels required) and perceptual scalability (the effectiveness of a visualization, measured in terms of user performance, as the amount of data being visualized is scaled-up). The purpose of this research was to compare information visualization designs for integrating spatial and multidimensional data in terms of their visual scalability for large, high resolution displays. Toward that goal a hierarchical design space was articulated and a series of user experiments were performed. A baseline was established by comparing user performance with opposing visualizations on a desktop monitor. Then, visualizations were compared as more information was added using the additional pixels available with a large, high resolution display. Results showed that integrated views were more visually scalable than multiple view visualizations. The visualizations tested were even scalable beyond the limits of visual acuity. User performance on certain tasks improved due to the additional information that was visualized even on a display with enough pixels to require physical navigation to visually distinguish all elements. The reasons for the benefits of integrated views on large, high resolution displays include a reduction in navigation due to spatial grouping and visual aggregation resulting in the emergence of patterns. These findings can help with the design of information visualizations for large, high resolution displays. / Ph. D.

Information visualizations utilizing multiple coordinated views allow users to rapidly explore complex data spaces and discover complex relationships. Most multiple-view visualizations are static with regard to the views that they provide and the coordinations that they support. Despite significant progress in the field of Information Visualization and development of novel interaction techniques, user interfaces for exploring data have lacked flexibility. As a result, the vast quantities of information rapidly being collected in databases are underutilized and opportunities for advancement of knowledge are lost. This research proposes the central concept of visualization schemas based on the Snap-Together Visualization (Snap) model, analogous to the successful database concept of data schemas, which will enable dynamic specification of information visualizations for any given database without programming. Relational databases provide significant flexibility to organize, store, and manipulate an infinite variety of complex data collections. This flexibility is enabled by the concept of relational data schemas, which allow data owners to easily design custom databases according to their unique needs. We bring the same level of flexibility to visualization development through visualization schemas. Visualization Schemas is a conceptual model, user interface, and software architecture while Fusion is the implemented system that enable users to rapidly and dynamically construct personalized multi-view visualization workspaces by coordinating visualizations in ways unforeseen by the original developers. / Master of Science

Information visualizations must often be custom programmed to support complex user tasks and database schemas. This is an expensive and time consuming effort, even when general-purpose visualizations are utilized within the solution. This research introduces the Snap visualization server and system architecture that addresses limitations of previous Snap-Together Visualization research and satisfies the need for flexibility in information visualizations. An enhanced visualization model is presented that formalizes multiple-view visualization in terms of the relational data model. An extensible architecture is introduced that enables flexible construction and component integration. It allows the integration of diverse data, letting users spend less time massaging the data prior to visualization. The web-based server enables universal access, easy distribution, and the ability to intermix and exploit existing components. This web-based software architecture provides a strong foundation for future multiple-view visualization development. / Master of Science

Information visualization tools provide visual representations of data (commonly known as visualizations), textual representations of data and interactive operations on both these representations. It is possible, in theory, to make use of only the textual representations in order to detect trends and patterns in data. However, it would be extremely laborious and ineffective and it defeats the purpose of a visualization tool. Novices have had the problem of relying on text and failing to successfully detect trends in data because it was too laborious. We want users to effectively use visual representations to detect trends in data. Information visualization tools have been shown to be successful with experts. But can novice users using a visualization tool for the first-time, adopt visualization-based strategies to finding trends and patterns in data? This thesis derives a framework of learnable elements in an interactive coordinated-view visualization tool. This framework provides an outline of prerequisites to be learned in order to effectively use visualizations. That is, the new aspects of visualization tools must be mastered so that novices can use the tool effectively. Three interface design principles are derived to make these elements learnable to novice users: • The data-first approach - Provide a prominent overview of all available data, as opposed to showing only visualizations. This helps understand the data structure - this may be essential knowledge in being able to navigate to required data attributes. • The "less visualization, more explanation" approach - Show more explanations of visualizations at the expense of being able to pack in more visualizations. Explanations help identify how data is mapped onto visual marks, a crucial step in understanding visualizations. • The "predetermined task-based coordinations" approach - Provide separate predetermined sets of coordinated visualizations that help achieve different tasks. This is different from techniques that simply present all visualization types and expect users to choose coordinations according to tasks. Two versions of Datamaps visualization tool for Census data were tested. Both were equivalent in functionality and the kinds of visualizations offered. But the new version was specifically designed based on the three design principles. A usability study showed that the version that was implemented according to the three design principles successfully led novices to effectively use visualization-based strategies to detect trends and patterns in data. / Master of Science