Matching statistics were introduced to solve the approximate string matching problem, which is a recurrent subroutine in bioinformatics applications. In 2010, Ohlebusch et al. [SPIRE 2010] proposed a time and space efficient algorithm for computing matching statistics which relies on some components of a compressed suffix tree - notably, the longest common prefix (LCP) array. In this paper, we show how their algorithm can be generalized from strings to Wheeler deterministic finite automata. Most importantly, we introduce a notion of LCP array for Wheeler automata, thus establishing a first clear step towards extending (compressed) suffix tree functionalities to labeled graphs.
Conte, A., Cotumaccio, N., Gagie, T., Manzini, G., Prezza, N., Sciortino, M. (2023). Computing matching statistics on Wheeler DFAs. In A. Bilgin, M.W. Marcellin, J. Serra-Sagrista, J.A. Storer (a cura di), Data Compression Conference 2023 (pp. 150-159). 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA : IEEE COMPUTER SOC [10.1109/DCC55655.2023.00023].
Computing matching statistics on Wheeler DFAs
Sciortino, M
2023-01-01
Abstract
Matching statistics were introduced to solve the approximate string matching problem, which is a recurrent subroutine in bioinformatics applications. In 2010, Ohlebusch et al. [SPIRE 2010] proposed a time and space efficient algorithm for computing matching statistics which relies on some components of a compressed suffix tree - notably, the longest common prefix (LCP) array. In this paper, we show how their algorithm can be generalized from strings to Wheeler deterministic finite automata. Most importantly, we introduce a notion of LCP array for Wheeler automata, thus establishing a first clear step towards extending (compressed) suffix tree functionalities to labeled graphs.File | Dimensione | Formato | |
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