Emergent Mind

Abstract

The model of generalized automata, introduced by Eilenberg in 1974, allows representing a regular language more concisely than conventional automata by allowing edges to be labeled not only with characters, but also strings. Giammaresi and Montalbano introduced a notion of determinism for generalized automata [STACS 1995]. While generalized deterministic automata retain many properties of conventional deterministic automata, the uniqueness of a minimal generalized deterministic automaton is lost. In the first part of the paper, we show that the lack of uniqueness can be explained by introducing a set $ \mathcal{W(A)} $ associated with a generalized automaton $ \mathcal{A} $. By fixing $ \mathcal{W(A)} $, we are able to derive for the first time a full Myhill-Nerode theorem for generalized automata, which contains the textbook Myhill-Nerode theorem for conventional automata as a degenerate case. In the second part of the paper, we show that the set $ \mathcal{W(A)} $ leads to applications for pattern matching and data compression. Wheeler automata [TCS 2017, SODA 2020] are a popular class of automata that can be compactly stored using $ e \log \sigma (1 + o(1)) + O(e) $ bits ($ e $ being the number of edges, $ \sigma $ being the size of the alphabet) in such a way that pattern matching queries can be solved in $ \tilde{O}(m) $ time ($ m $ being the length of the pattern). In the paper, we show how to extend these results to generalized automata. More precisely, a Wheeler generalized automata can be stored using $ \mathfrak{e} \log \sigma (1 + o(1)) + O(e + rn) $ bits so that pattern matching queries can be solved in $ \tilde{O}(r m) $ time, where $ \mathfrak{e} $ is the total length of all edge labels, $ r $ is the maximum length of an edge label and $ n $ is the number of states.

We're not able to analyze this paper right now due to high demand.

Please check back later (sorry!).

Generate a summary of this paper on our Pro plan:

We ran into a problem analyzing this paper.

Newsletter

Get summaries of trending comp sci papers delivered straight to your inbox:

Unsubscribe anytime.