The Burrows-Wheeler Transform is a text permutation that has revolutionized the fields of pattern matching and text compression, bridging the gap existing between the two. In this paper we approach the BWT-construction problem generalizing a well-known algorithm—based on backward search and dynamic strings manipulation—to work in a context-wise fashion, using automata on words. Let n , σ , and Hk be the text length, the alphabet size, and the k -th order empirical entropy of the text, respectively. Moreover, let H∗k=min{Hk+1,⌈logσ⌉} . Under the word RAM model with word size w∈Θ(logn) , our algorithm builds the BWT in average O(nH∗k) time using nH∗k+o(nH∗k) bits of space, where k=log_σ(n/log2n)−1 . We experimentally show that our algorithm has very good performances (essentially linear time) on DNA sequences, using about 2.6 bits per input symbol in RAM.
