What Is Burrows-Wheeler Transform (BWT)?
We are not talking about the latest wheeler-dealer car show on your tv. It is much more complicated than that but easy once you understand. When compressing data, you want to keep only the valuable information. The Burrows-Wheeler transform (BWT) rearranges your data blocks, making them easier to compress. You'll get the same output but with less space used. It's a common technique that you'll find in many algorithms for compressing text and binary files. The algorithm was first published in 1994 by Michael L. Burrows and Daniel P. Wheeler, who described it as a "fast algorithm for generating approximate probability distributions". Since then, it has been widely implemented in many different applications. This article will uncover the basics of the algorithm and then look at how you can use it in your applications. Where `block` is the data to be BWT encoded, `eof` is the last character to be added to `block,` and `temp` is the current character being examined, Initialise `temp` to `block[0]`. While `temp` does not have the value of `Eof`, do the following: a. Calculate the frequency of the next character of `temp` in the data set. b. If the frequency exceeds a threshold, swap `temp` with the following block surface. In the most basic, BWT takes a block of data, such as a string and adds an EOF character to each letter. Then it sorts all rotations of the series into lexicographical order. The Burrows-Wheeler Transform (BWT) is a compression method that improves the stored string size and can be used as input to many other compression methods. The BWT takes a line and "transfers" it to a new location using repeated substitution. Each repeated sub-string is bunched together, making it easier to compress with other techniques. The Burrows-Wheeler transform, or BWT for short, is a simple but powerful algorithm.
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