This thesis addresses remote distributed rendering in grid environments. The research, and resulting software tools are merited according to the improvements afforded to application scientists. This includes the portability and transparent methods with which the rendering systems can be incorporated into existing visualisations. A discussion of existing research in the domains of remote visualisation and parallel rendering is presented. In doing so, the thesis examines relevant technology from other domains including Internet video streaming. This research is then extrapolated into the domain of interactive distributed rendering. From this, a new Stream Adaptive Rendering (StreamAR) framework is developed. The StreamAR framework is implemented as visualisation service grid, providing the unique capability to dynamically adapt the rendering process of bespoke applications across distributed resources to maintain the quality-of-service requirements of a visualisation user.

A brief summary of job control language development precedes a general discussion of possible improvements in command language practice. The user requirements of a command language are considered with special reference to a machine independent basis. "Primitive" functions are defined from this viewpoint. To meet the proposed objective of portability it is suggested that an appreciation of the user interaction with the computer operating system is necessary. This provides the definition of the user profile model based on the user requirements of a command language. A second model is then developed to represent the structure of the operating system.

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The advent of mobile and web technologies has given rise to unlimited numbers of concurrent users executing their transactions in databases in continuous streams. In order to prove correctness, a method of modelling the behaviour of such transactions is required. Most approaches to proving the correctness of the concurrent execution of transactions, have relied on mathematical proofs. These have numerous disadvantages such as: the person who performs the proof needs to be an expert in mathematical proof techniques, the possibility of human error occurring in manual proofs, and if a simplified model is used not all system behaviours are covered and not all properties can be proved. With regard to the last point, most models assume a fixed finite number of transactions. In the first part of this thesis, we present a model of an unlimited number of multi-step transactions occurring in web and mobile environments over time, where a finite number of the possible different transactions repeat or 'iterate' infinitely often.

This thesis describes the design and development of high level language programming systems for use on a small mini-computer installation in an educational environment. Any high level language may be implemented in a number of different ways. The major alternatives are reviewed with particular reference to hardware resources, ease of use and the need to cater for both on-line and off-line applications. The implementation of a programming system based upon the use of an interpreter rather than a compiler is described. The system enables a single user to create develop and execute programs for applications involving on-line data acquisition and control. Hardware resources are most effectively utilized in a time shared rather than a dedicated mode. The problems of extending this system to permit time sharing are discussed and an appropriate solution is described. In a situation where it is necessary to segregate data processing from data acquisition, the use of a common programming system is desirable. An off-line operating system is described which adds file handling capabilities to the interpreter and provides a common software interface between data acquisition and data processing programs.

The study of event streams involves modelling arrival times of discrete random events. Predictive systems for event streams infer information about future occurrences of random events. These systems are useful for various applications such as stock trading modelling and earthquake analysis, but the computational burdens limit their predictive power. This thesis addresses the design and optimisation of reconfigurable predictive systems for event streams. The first contribution of this thesis is the reconfigurable acceleration solution for the calculations in the Hawkes point process models. We propose reconfigurable dataflow engines for intensity evaluation and likelihood evaluation for univariate and multivariate Hawkes point process models. We also establish an efficient collaboration scheme between the CPU platform and the reconfigurable accelerator to speed up parameter estimation. The second contribution of this thesis is a novel predictive model for event streams. As the Hawkes point process models have limited predictive accuracy and low computational efficiency, we propose a predictive model for event streams using regression techniques. We derive the model from the intensity function of the Hawkes point process, but the final form of the proposed model works without point process models. A software system based on the proposed model reduces the prediction error by 3\%--7\% compared to a system based on the Hawkes process. A hardware-accelerated system based on the proposed model is 5--66 times faster in model fitting compared to the accelerated system based on the Hawkes process, while the two systems produce similar predictive accuracy. The third contribution of this thesis is the design of two reconfigurable accelerators for time series analysis. One accelerator is for the estimation of correlation for multivariate time series. The other accelerator is for the ordinal pattern encoding for univariate time series. We design the two accelerators by transforming the calculations of the corresponding statistical metrics into pipeline-friendly forms. Compared to multicore CPUs, both accelerators show high efficiency for large time series data.

This research examines the use of a computer mouse by motion-impaired users, with an aim to improving GUI interactions through an understanding of movement and investigations into the use of haptic feedback as a form of assistance. This research proposes measures, derived from a submovement representation of movement, that are appropriate for motion-impaired users, and applies them in a study of cursor trajectories. The measures are shown to be sensitive to differences between users with dissimilar physical capabilities, and can provide insight into the ways in which motion-impaired users differ from able-bodied users. This research also investigates the use of haptic feedback, provided through a low-cost force feedback mouse, as a means of assisting motion-impaired users with GUI interactions. Haptic interfaces provide feedback to the user through the sense of touch. The use of haptic feedback with single targets is investigated first. Three effects are studied: gravity wells, tunnels, and damping. Results show that these effects can reduce times and errors in a target selection task, but the degree of benefit is related to a user’s characteristic movement. As GUI interactions typically involve selecting from one of multiple targets on the screen, this research also includes four studies investigating situations where multiple targets are all enabled with gravity wells. Results show that the haptic feedback can provide benefits in terms of times and errors, but in some cases, can also be detrimental to performance.

Motion Estimation is an important research field with many commercial applications including surveillance, navigation, robotics, and image compression. As a result, the field has received a great deal of attention and there exist a wide variety of Motion Estimation techniques which are often specialised for particular problems. The relative performance of these techniques, in terms of both accuracy and of computational requirements, is often found to be data dependent, and no single technique is known to outperform all others for all applications under all conditions. Information Fusion strategies seek to combine the results of different classifiers or sensors to give results of a better quality for a given problem than can be achieved by any single technique alone. Information Fusion has been shown to be of benefit to a number of applications including remote sensing, personal identity recognition, target detection, forecasting, and medical diagnosis. This thesis proposes and demonstrates that Information Fusion strategies may also be applied to combine the results of different Motion Estimation techniques in order to give more robust, more accurate and more timely motion estimates than are provided by any of the individual techniques alone. Information Fusion strategies for combining motion estimates are investigated and developed. Their usefulness is first demonstrated by combining scalar motion estimates of the frequency of rotation of spinning biological cells. Then the strategies are used to combine the results from three popular 2D Motion Estimation techniques, chosen to be representative of the main approaches in the field. Results are presented, from both real and synthetic test image sequences, which illustrate the potential benefits of Information Fusion to Motion Estimation applications. There is often a trade-off between accuracy of Motion Estimation techniques and their computational requirements. An architecture for Information Fusion that allows faster, less accurate techniques to be effectively combined with slower, more accurate techniques is described. This thesis describes a number of novel techniques for both Information Fusion and Motion Estimation which have potential scope beyond that examined here. The investigations presented in this thesis have also been reported in a number of workshop, conference and journal papers, which are listed at the end of the document.

Type systems for programming languages can be used by compilers to reject programs which are found to be unsound and which may, therefore, fail to execute successfully. When a program is rejected the programmer must repair it so that it can be type-checked correctly and then executed safely. Diagnostic error messages are essential to help the programmer repair the program. Hindley-Milner type systems give the programmer a great deal of flexibility (polymorphism and implicit typing) while ensuring type safety. As a consequence of this flexibility repairing mistakes can be difficult and programmers have previously observed that type error messages produced by compilers are not helpful enough. This thesis examines the problem of producing more helpful error messages for ill-typed programs written in programming languages with a Hindley-Milner typing discipline. Three main results are described. Firstly, type inference algorithms which infer types in different orders are described, and the ability of these to produce more meaningful error messages is investigated. Secondly, the results of several other authors on helping to explain type inference are condensed into a single generalisation. Thirdly, error messages which suggest concrete changes to the program to remove type errors are produced using the theory of linear type isomorphisms. This theory is implemented as an extension to the MLj compiler. Finally, extensions to Hindley-Milner are explored, taking the type system of MLj as an example.

Software developers working on unfamiliar systems are challenged to identify where and how high-level concepts are implemented in the source code prior to performing maintenance tasks. Bug localisation is a core program comprehension activity in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source files as the documents to be retrieved, ranked by relevance. Current approaches rely on project history, in particular previously fixed bugs and versions of the source code. Existing IR techniques fall short of providing adequate solutions in finding all the source code files relevant for a bug. Without additional help, bug localisation can become a tedious, time- consuming and error-prone task. My research contributes a novel algorithm that, given a bug report and the application’s source files, uses a combination of lexical and structural information to suggest, in a ranked order, files that may have to be changed to resolve the reported bug without requiring past code and similar reports. I study eight applications for which I had access to the user guide, the source code, and some bug reports. I compare the relative importance and the occurrence of the domain concepts in the project artefacts and measure the effectiveness of using only concept key words to locate files relevant for a bug compared to using all the words of a bug report. Measuring my approach against six others, using their five metrics and eight projects, I position an effected file in the top-1, top-5 and top-10 ranks on average for 44%, 69% and 76% of the bug reports respectively. This is an improvement of 23%, 16% and 11% respectively over the best performing current state-of-the-art tool. Finally, I evaluate my algorithm with a range of industrial applications in user studies, and found that it is superior to simple string search, as often performed by developers. These results show the applicability of my approach to software projects without history and offers a simpler light-weight solution.