Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.

Immunostimulatory effects of different aspects of aquaculture on the host response in the edible sea urchin, paracentrotus lividus

Aquaculture is a fast-growing industry contributing to global food security and sustainable aquaculture, which may reduce pressures on capture fisheries. The overall objective of this thesis was to look at the immunostimulatory effects of different aspects of aquaculture on the host response of the edible sea urchin, Paracentrotus lividus, which are a prized delicacy (roe) in many Asian and Mediterranean countries. In Chapter 1, the importance of understanding the biology, ecology, and physiology of P. lividus, as well as the current status in the culture of this organism for mass production and introducing the thesis objectives for following chapters is discussed. As the research commenced, the difficulties of identifying individuals for repeat sampling became clear; therefore, Chapter 2 was a tagging experiment that indicated PIT tagging was a successful way of identifying individual sea urchins over time with a high tag retention rate. However, it was also found that repeat sampling via syringe to measure host response of an individual caused stress which masked results and thus animals would be sampled and sacrificed going forward. Additionally, from personal observations and discussion with peers, it was suggested to look at the effect that diet has on sea urchin immune function and the parameters I measured which led to Chapter 3. In this chapter, both Laminaria digitata and Mytilus edulis were shown to influence measured immune parameters of differential cell counts, nitric oxide production, and lysozyme activity. Therefore, trials commencing after Trial 5 in Chapter 4, were modified to include starvation in order to remove any effect of diet. Another important aspect of culturing any organism is the study of their immune function and its response to several immunostimulatory agents (Chapter 4). Zymosan A was shown to be an effective immunostimulatory agent in P. lividus. Further work on handled/stored animals (Chapter 5) showed Zymosan A reduced the measured levels of some immune parameters measured relative to the control, which may reduce the amount of stress in the animals. In Chapter 6, animals were infected with Vibrio anguillarum and, although V. anguillarum, impacted immune parameters of P. lividus, it did not cause mortality as predicted. Lastly, throughout this thesis work, it was noted that the immune parameters measured produced different values at different times of the year (Chapter 7); therefore, using collated baseline (control) data, results were compiled to observe seasonal effects. It was determined that both seasonality and sourcing sites influenced immune parameter measurements taken at different times throughout the year. In conclusion, this thesis work fits into the framework of development of aquaculture practices that affect immune function of the host and future research focusing on the edible sea urchin, P. lividus.

Modelling and predictive control techniques for building heating systems

Model predictive control (MPC) has often been referred to in literature as a potential method for more efficient control of building heating systems. Though a significant performance improvement can be achieved with an MPC strategy, the complexity introduced to the commissioning of the system is often prohibitive. Models are required which can capture the thermodynamic properties of the building with sufficient accuracy for meaningful predictions to be made. Furthermore, a large number of tuning weights may need to be determined to achieve a desired performance. For MPC to become a practicable alternative, these issues must be addressed. Acknowledging the impact of the external environment as well as the interaction of occupants on the thermal behaviour of the building, in this work, techniques have been developed for deriving building models from data in which large, unmeasured disturbances are present. A spatio-temporal filtering process was introduced to determine estimates of the disturbances from measured data, which were then incorporated with metaheuristic search techniques to derive high-order simulation models, capable of replicating the thermal dynamics of a building. While a high-order simulation model allowed for control strategies to be analysed and compared, low-order models were required for use within the MPC strategy itself. The disturbance estimation techniques were adapted for use with system-identification methods to derive such models. MPC formulations were then derived to enable a more straightforward commissioning process and implemented in a validated simulation platform. A prioritised-objective strategy was developed which allowed for the tuning parameters typically associated with an MPC cost function to be omitted from the formulation by separation of the conflicting requirements of comfort satisfaction and energy reduction within a lexicographic framework. The improved ability of the formulation to be set-up and reconfigured in faulted conditions was shown.