The reconstruction of a function and its derivative from a set of measured samples is a fundamental operation in visualisation. Multiresolution techniques, such as wavelet signal processing, are instrumental in improving the performance and algorithm design for data analysis, filtering and processing. This dissertation explores the possibilities of combining traditional multiresolution analysis and processing features of wavelets with the design of appropriate filters for reconstruction of sampled data. On the one hand, a multiresolution system allows data feature detection, analysis and filtering. Wavelets have already been proven successful in these tasks. On the other hand, a choice of discrete filter which converges to a continuous basis function under iteration permits efficient and accurate function representation by providing a “bridge” from the discrete to the continuous. A function representation method capable of both multiresolution analysis and accurate reconstruction of the underlying measured function would make a valuable tool for scientific visualisation. The aim of this dissertation is not to try to outperform existing filters designed specifically for reconstruction of sampled functions. The goal is to design a wavelet filter family which, while retaining properties necessary to preform multiresolution analysis, possesses features to enable the wavelets to be used as efficient and accurate “building blocks” for function representation. The application to visualisation is used as a means of practical demonstration of the results. Wavelet and visualisation filter design is analysed in the first part of this dissertation and a list of wavelet filter design criteria for visualisation is collated. Candidate wavelet filters are constructed based on a parameter space search of the BC-spline family and direct solution of equations describing filter properties. Further, a biorthogonal wavelet filter family is constructed based on point and average interpolating subdivision and using the lifting scheme. The main feature of these filters is their ability to reconstruct arbitrary degree piecewise polynomial functions and their derivatives using measured samples as direct input into a wavelet transform. The lifting scheme provides an intuitive, interval-adapted, time-domain filter and transform construction method. A generalised factorisation for arbitrary primal and dual order point and average interpolating filters is a result of the lifting construction. The proposed visualisation filter family is analysed quantitatively and qualitatively in the final part of the dissertation. Results from wavelet theory are used in the analysis which allow comparisons among wavelet filter families and between wavelets and filters designed specifically for reconstruction for visualisation. Lastly, the performance of the constructed wavelet filters is demonstrated in the visualisation context. One-dimensional signals are used to illustrate reconstruction performance of the wavelet filter family from noiseless and noisy samples in comparison to other wavelet filters and dedicated visualisation filters. The proposed wavelet filters converge to basis functions capable of reproducing functions that can be represented locally by arbitrary order piecewise polynomials. They are interpolating, smooth and provide asymptotically optimal reconstruction in the case when samples are used directly as wavelet coefficients. The reconstruction performance of the proposed wavelet filter family approaches that of continuous spatial domain filters designed specifically for reconstruction for visualisation. This is achieved in addition to retaining multiresolution analysis and processing properties of wavelets.

In recent years, the focus of cartographic research has shifted from the cartographic communication paradigm to the scientific visualization paradigm. With this, there has been a resurgence of cognitive research that is invaluable in guiding the design and evaluation of effective cartographic visualization tools. The design of new tools that allow effective visual exploration of spatial data and data quality information in a resource management setting is critical if decision-makers and policy setters are to make accurate and confident decisions that will have a positive long-term impact on the environment. The research presented in this dissertation integrates the results of previous research in spatial cognition, visualization of spatial information and on-line map use in order to explore the design, development and experimental testing of four interactive visualization tools that can be used to simultaneously explore spatial data and data quality. Two are traditional online tools (side-by-side and sequenced maps) and two are newly developed tools (an interactive "merger" bivariate map and a hybrid o f the merger map and the hypermap). The key research question is: Are interactive visualization tools, such as interactive bivariate maps and hypermaps, more effective for communicating spatial information than less interactive tools such as sequenced maps? A methodology was developed in which subjects used the visualization tools to explore a forest species composition and associated data quality map in order to perform a range of map-use tasks. Tasks focused on an imaginary land-use conflict for a small region of mixed boreal forest in Northern Alberta. Subject responses in terms of performance (accuracy and confidence) and preference are recorded and analyzed. Results show that theory-based, well-designed interactive tools facilitate improved performance across all tasks, but there is an optimal matching between specific tasks and tools. The results are generalized into practical guidelines for software developers. The use of confidence as a measure of map-use effectiveness is verified. In this experimental setting, individual differences (in terms of preference, ability, gender etc.) did not significantly affect performance. / Arts, Faculty of / Geography, Department of / Graduate

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 Computing / M. Sc. (Computing)

As computing technology continues to advance, it has become increasingly difficult to find businesses that do not rely, at least in part, upon the collection and analysis of data for the purpose of project management and process improvement. The cost of software tends to increase over time due to its complexity and the cost of employing humans to develop, maintain, and evolve it. To help control the costs, organizations often seek to improve the process by which software systems are developed and evolved. Improvements can be realized by discovering previously unknown or hidden relationships between the artifacts generated as a result of developing a software system. The objective of the work described in this thesis is to provide a visualization tool that helps managers and engineers better plan for future projects by discovering new knowledge gained by synthesizing and visualizing data mined from software repository records from previous projects. / by James J. Mulcahy. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

Bibliography: leaves 173-184. This thesis describes Visor++, a tool for visualising programs written in CC++, a task-parallel, object-orientated language derived from C++. Visor++ provides a framework of visualising task-parallel object-orientated programs in the absence of language support for visualisation, i.e. for programs such as CC++ which are written in languages which are not "visualisation-conscious". The development of techniques using a wide selection of language features are described and the effectiveness testified by experimentation.

Complex patient are becoming more and more of a challenge to the health care system given the amount of care they require and the amount of documentation needed to keep track of their state of health and treatment. Record keeping using the EHR makes this easier but mounting amounts of patient data also means that clinicians are faced with information overload. Information overload has been shown to have deleterious effects on care, with increased safety concerns due to missed information. Patient record summarization has been a promising mitigator for information overload. Subsequently, a lot of research has been dedicated to record summarization since the introduction of EHRs. In this dissertation we examine whether unsupervised inference methods can derive patient problem-oriented summaries, that are robust to different patients. By grounding our experiments with HIV patients we leverage the data of a group of patients that are similar in that they share one common disease (HIV) but also exhibit complex histories of diverse comorbidities. Using a user-centered, iterative design process, we design an interactive, longitudinal patient record summarization tool, that leverages automated inferences about the patient's problems. We find that unsupervised, joint learning of problems using correlated topic models, adapted to handle the multiple data types (structured and unstructured) of the EHR, is successful in identifying the salient problems of complex patients. Utilizing interactive visualization that exposes inference results to users enables them to make sense of a patient's problems over time and to answer questions about a patient more accurately and faster than using the EHR alone.

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M. Tech. (Department of Information and Communications Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Global companies tend to have problems in knowing the systems, assets and resources they have within their global footprint. This tends to be costly to the company as there tends to be purchases of the same systems, assets and resources that are already available in another business unit or department within the company. This leads to money being wasted on research, procurement and/or training, to name a few. You can't manage what you don't know about. This thesis will show how the visualisation of corporate data is possible and has sustainable benefit to a company. Knowing the status of the Information and Communications Technology (ICT) environment in a company at some point in time is crucial in planning and implementing strategies with the future in mind. Global View, which will be explained in this thesis, is key in facilitating the extracting of information from all the sites globally and presenting the information in a logical and structured way. Information gathering from these sites using Global View needs to be done in an auditing fashion with the focus on accurate and precise information with an option which would allow the audit to be a continuous and automatic process. Global View is a tool to facilitate the implementation of ICT Industrialization through a process of gathering the relevant ICT information worldwide with specific reference to each site. Global View displays the relevant information from systems, assets, resources and environments in an easily accessible format. The functionality that is built into the concept in this thesis is the geographical and geospatial orientation which is linked to the relevant sites as well as time references related to Greenwich Mean Time to make users aware of differences in time when communicating with people in other countries either individually or multiple sites simultaneously.

This thesis describes a research project aimed at improving the tracing skills of first-year programming students enrolled for an introductory C++ course at an open distance learning institution by means of a tutorial in the form of a program visualization tool to teach the students to draw variable diagrams. The research was based on the findings from the BRACElet project (Clear, Whalley, Robbins, Philpott, Eckerdal, Laakso & Lister, 2011). A design-based research methodology was followed. To guide the process of developing the tutorial, a framework of 26 guidelines for developing and using visualization tools to teach programming was synthesized from the literature on computing education research CER, educational psychology and computer graphics. Guidelines were supplemented with reasons or explanations for their recommendation and considerations to be taken into account when using a guideline. The framework was enhanced by lessons learnt during the development and testing of the tutorial. The tutorial was tested and refined during two implementation cycles. Both cycles included quantitative and qualitative investigations. All students registered for the introductory module received the tool with their study material. For the quantitative investigations, students completed a questionnaire after using the tutorial. Through the questionnaire biographical data was acquired, the manner in which students used the tutorial and how they experienced using it. The responses to the questionnaires were statistically analysed in combination with respondents’ final marks. The statistical modelling indicated that the students’ biographical properties (a combination of level of programming experience, marks obtained for Mathematics and English in matric and first-time registration for COS1511 or not), had the biggest impact on their final marks by far. During the qualitative investigations students were eye tracked in a Human-Computer Interaction laboratory. The gaze replays in both cycles revealed that students’ reading skills impacted largely on their success, connecting with the findings from the quantitative investigations. Reflections on why the tutorial did not achieve its purpose; and why poor reading skills may have such a strong effect on learning to program, contribute some theoretical understanding as to how novices learn to program. / Computer Science / D. Phil. (Computer Science)

This study was motivated by the need to overcome the pedagogical hindrances experienced by introductory object-oriented programming students in order to address the high attrition rate evident among novice programmers in distance education. The initial phase of the research process involved exploring a variety of alternative visual programming environments for novices. Thereafter the selection process detailed several requirements that would define the ideal choice of the most appropriate tool. An educational tool Raptor was selected. Lastly, the core focus of this mixed method research was to evaluate undergraduate UNISA students’ perceptions of the Raptor e-learning tools with respect to the perceived effectiveness in enhancing novices’ learning experience, in an attempt to lower the barriers to object-oriented programming. Students’ perceptions collectively of the Raptor visual tool were positive and despite the fact that the sample size was too small to achieve statistical significance, these quantitative and qualitative results provide the practical basis for implementing Raptor in future. Thus providing learning opportunities suited to learner interests and needs, can lead to an enormous potential to stimulate individuals’ motivation and development in creating a more positive learning experience to overcome barriers in programming and enhance concept understanding to address the diverse needs of students in distance education that could lead to a reduced dropout rate. / Computing / M. Sc. (Computing)