An analysis of the Burrows-Wheeler transform

By Giovanni Manzini. ACM-SIAM Symposium on Discrete Algorithms, 2001

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Compressed full-text indexes (2007, methodology): “The Burrows-Wheeler Transform The Burrows-Wheeler Transform [Burrows and Wheeler 1994] is a reversible transformation from strings to strings.4 This transformed text is easier to compress by local optimization methods [Manzini 2001].”
Indexing Compressed Text (2003, methodology): “Assume that the maximum length of the compressed buckets is l′ bits, and according to [41], l′ = (1 + 2 lg Σ )l.”
Opportunistic data structures with applications (2000, methodology): “We compressT bw in three steps (see also [20]):”
Indexing compressed text (2005, methodology): “Our proof is based on some results about BWT-based compressors given in Manzini [2001]. Following the notation in Manzini [2001, Sec.”
On Prediction Using Variable Order Markov Models (2004, methodology): “(2003) and McAllester and Ortiz (2003), can help in devising a formal optimality measure for the ppm escape implementations. We can view the ctw algorithm as an ensemble of numerous VMMs. However, the weights of the various VMM models are fixed. Various ensemble methods in machine learning such as b”