Thesis (M. Sc.)--Acadia University, 2005. / Includes bibliographical references (leaves 80-83). Also available on the Internet via the World Wide Web.

Thesis (M.Sc.)--Acadia University, 2007. / Includes bibliographical references (leaves 72-75). Also available on the Internet via the World Wide Web.

Thesis (M.App.Sc.) - Carleton University, 2007. / Includes bibliographical references (p. 99-103). Also available in electronic format on the Internet.

Thesis (Ph. D.)--Duke University, 2007.

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains vii, 73 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 69-73).

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Nur Touba. Vita. Includes bibliographical references.

XML is a widely used data exchange format. The verbose nature of XML leads to the requirement to efficiently store and process this type of data using compression. Various general-purpose transforms and compression techniques exist that can be used to transform and compress XML data. More compact alternatives to XML data have been developed, namely JSON due to the verbosity of XML data. Similarly, there is a requirement to efficiently store and process SMILES data used in Chemoinformatics. General-purpose transforms and compressors can be used to compress this type of data to a certain extent, however, these techniques are not specific to SMILES data. The primary contribution of this research is to provide developers that use XML, JSON or SMILES data, with key knowledge of the best transformation techniques to use with certain types of data, and which compression techniques would provide the best compressed output size and processing times, depending on their requirements. The main study in this thesis, investigates the extent of which using data transforms prior to data compression can further improve the compression of XML and JSON data. It provides a comparative analysis of applying a variety of data transform and data transform variations, to a number of different types of XML and JSON equivalent datasets of various sizes, and applying different general-purpose compression techniques over the transformed data. A case study is also conducted, to investigate data transforms prior to compression to improve the compression of data within a data-specific domain.

[pt] Neste trabalho é estudado o problema da compressão de dados por codificadores de estado finito e sem perda de informação. O problema é dividido em três partes: compressão de seqüências individuais, compressão de pares de seqüências e compressão de imagens. A principal motivação do trabalho é o estudo da compressão de pares de seqüências, como um passo intermediário para o entendimento do problema da compressão de dados bidimensionais. Para cada um dos casos é definido um limitante inferior para a taxa de compressão de qualquer codificador de estado finito e sem perda de informação. Para os três casos, codificadores universais são propostos e seus desempenhos são analisados. Os codificadores propostos foram implementados em software e aplicados à compressão de seqüências finitas, pares de seqüências finitas e imagens finitas. Os resultados de simulação obtidos são analisados. / [en] In this work the problem of data compression by finite- state and information lossless encorders is studied. The problem is divided in three parts: compression of individual sequences, compression of pairs of sequences and compression of images. For each of these, a lower bound is defined which sets a limit on the smaller compression rate that can be achieved by any finite-state and information lossless enconders. Universal encorders are proposed and their performance compared to the optimal attainable. The proposed encoders were implemented in software and used to compress finite sequences, pairs of finite sequences and finite images. The simulation results are analysed.

With the move towards global and multi-national companies, information technology infrastructure requirements are increasing. As the size of these computer networks increases, it becomes more and more difficult to monitor, control, and secure them. Networks consist of a number of diverse devices, sensors, and gateways which are often spread over large geographical areas. Each of these devices produce log files which need to be analysed and monitored to provide network security and satisfy regulations. Data compression programs such as gzip and bzip2 are commonly used to reduce the quantity of data for archival purposes after the log files have been rotated. However, there are many other compression programs which exist - each with their own advantages and disadvantages. These programs each use a different amount of memory and take different compression and decompression times to achieve different compression ratios. System log files also contain redundancy which is not necessarily exploited by standard compression programs. Log messages usually use a similar format with a defined syntax. In the log files, all the ASCII characters are not used and the messages contain certain "phrases" which often repeated. This thesis investigates the use of compression as a means of data reduction and how the use of semantic knowledge can improve data compression (also applying results to different scenarios that can occur in a distributed computing environment). It presents the results of a series of tests performed on different log files. It also examines the semantic knowledge which exists in maillog files and how it can be exploited to improve the compression results. The results from a series of text preprocessors which exploit this knowledge are presented and evaluated. These preprocessors include: one which replaces the timestamps and IP addresses with their binary equivalents and one which replaces words from a dictionary with unused ASCII characters. In this thesis, data compression is shown to be an effective method of data reduction producing up to 98 percent reduction in filesize on a corpus of log files. The use of preprocessors which exploit semantic knowledge results in up to 56 percent improvement in overall compression time and up to 32 percent reduction in compressed size. / TeX / pdfTeX-1.40.3

[pt] A tese de mestrado, aqui resumida, tem a meta de propor novos algoritmos para a compressão de dados, em especial imagens, apresentando aplicações e resultados teóricos. Como o título sugere, estes dados serão originados de fontes com alfabeto contínuo, podendo ser particularizado para os casos discretos. O algoritmo a ser proposto (LLZ para o caso contínuo) é baseado no codificador universal Lempel-Ziv, apresentando a característica de admitir a introdução de perdas, mas que conduza a um maior aproveitamento do poder de compressão deste algoritmo. Desta forma, o LLZ se mostra vantajoso em dois pontos: integrar compactador e quantizador, e ser um quantizador universal. / [en] This dissertation introduces new data compression algorithms, specially for images, and presents some applications and theoretical results related to these algorithms. The data to be compressed will be originated from sources with continuos alphabet, and can be stated for discrete sources. The proposed algorithms (LLZ for continuos case), which is based on the universal Lempel-Ziv coder (LZ), accepts losses, taking advantage of LZ s compression power. As such, the LIZ is an innovating proposal in two ways: first, it couples compaction and quantization in one step; and second, it can be seeing as an universal quantizer.