Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Nur A. Touba. Vita. Includes bibliographical references. Available also from UMI company.

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains vii, 56 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 52-54).

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Dr. J. R. Jackson; Committee Member: Dr. D. Scott; Committee Member: Dr. D. V. Anderson; Committee Member: Dr. P. Vela; Committee Member: Dr. R. Mersereau. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Computer Science." Includes bibliographical references (p. 80-83).

Thesis (M.Sc. (Computer Science)) - Rhodes University, 2009.

Thesis (Ph. D.)--University of Washington, 1996. / Vita. Includes bibliographical references (p. [150]-155).

Thesis (M.S.)--West Virginia University, 2010. / Title from document title page. Document formatted into pages; contains v, 61 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 54-56).

Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains vii, 90 p. : ill. Includes abstract. Includes bibliographical references (p. 84-90).

Thesis (MTech (Information Technology))--Cape Technikon, Cape Town, 2000 / Data stored on disks generally contain significant redundancy. A mechanism or algorithm that recodes the data to lessen the data size could possibly double or triple the effective data that could be stored on the media. One mechanism of doing this is by data compression. Many compression algorithms currently exist, but each one has its own advantages as well as disadvantages. The objective of this study', to formulate a new compression algorithm that could be implemented in a real-time mode in any file system. The new compression algorithm should also execute as fast as possible, so as not to cause a lag in the file systems performance. This study focuses on binary data of any type, whereas previous articles such as (Huftnlan. 1952:1098), (Ziv & Lempel, 1977:337: 1978:530), (Storer & Szymanski. 1982:928) and (Welch, 1984:8) have placed particular emphasis on text compression in their discussions of compression algorithms for computer data. The resulting compression algorithm that is formulated by this study is Lempel-Ziv-Toutlc (LZT). LZT is basically an LZ77 (Ziv & Lempel, 1977:337) encoder with a buffer size equal in size to that of the data block of the file system in question. LZT does not make this distinction, it discards the sliding buffer principle and uses each data block of the entire input stream. as one big buffer on which compression can be performed. LZT also handles the encoding of a match slightly different to that of LZ77. An LZT match is encoded by two bit streams, the first specifying the position of the match and the other specifying the length of the match. This combination is commonly referred to as a <position, length> pair. To encode the position portion of the <position, length> pair, we make use of a sliding scale method. The sliding scale method works as follows. Let the position in the input buffer, of the current character to be compressed be held by inpos, where inpos is initially set to 3. It is then only possible for a match to occur at position 1 or 2. Hence the position of a match will never be greater than 2, and therefore the position portion can be encoded using only 1 bit. As "inpos" is incremented as each character is encoded, the match position range increases and therefore more bits will be required to encode the match position. The reason why a decimal 2 can be encoded 'sing only I bit can be explained as follows. When decimal values are converted to binary values, we get 010 = 02, 110 = 12, 210, = 102etc. As a position of 0 will never be used, it is possible to develop a coding scheme where a decimal value of 1 can be represented by a binary value of 0, and a decimal value of 2 can be represented by binary value of 1. Only I bit is therefore needed to encode match position I and match position 2. In general. any decimal value n ca:) be represented by the binary equivalent for (n - 1). The number of bits needed to encode (n - 1), indicates the number of bits needed to encode the match position. The length portion of the <position, length> pair is encoded using a variable length coding (vlc) approach. The vlc method performs its encoding by using binary blocks. The first binary block is 3 bits long, where binary values 000 through 110 represent decimal values I through 7.

Burrows-Wheeler compression is a three stage process in which the data is transformed with the Burrows-Wheeler Transform, then transformed with Move-To-Front, and finally encoded with an entropy coder. Move-To-Front, Transpose, and Frequency Count are some of the many algorithms used on the List Update problem. In 1985, Competitive Analysis first showed the superiority of Move-To-Front over Transpose and Frequency Count for the List Update problem with arbitrary data. Earlier studies due to Bitner assumed independent identically distributed data, and showed that while Move-To-Front adapts to a distribution faster, incurring less overwork, the asymptotic costs of Frequency Count and Transpose are less. The improvements to Burrows-Wheeler compression this work covers are increases in the amount, not speed, of compression. Best x of 2x-1 is a new family of algorithms created to improve on Move-To-Front's processing of the output of the Burrows-Wheeler Transform which is like piecewise independent identically distributed data. Other algorithms for both the middle stage of Burrows-Wheeler compression and the List Update problem for which overwork, asymptotic cost, and competitive ratios are also analyzed are several variations of Move One From Front and part of the randomized algorithm Timestamp. The Best x of 2x - 1 family includes Move-To-Front, the part of Timestamp of interest, and Frequency Count. Lastly, a greedy choosing scheme, Snake, switches back and forth as the amount of compression that two List Update algorithms achieves fluctuates, to increase overall compression. The Burrows-Wheeler Transform is based on sorting of contexts. The other improvements are better sorting orders, such as “aeioubcdf...” instead of standard alphabetical “abcdefghi...” on English text data, and an algorithm for computing orders for any data, and Gray code sorting instead of standard sorting. Both techniques lessen the overwork incurred by whatever List Update algorithms are used by reducing the difference between adjacent sorted contexts.