A Searchable Compressed Edit-Sensitive Parsing

TL;DR

This paper introduces a practical, compressed data structure for edit-sensitive parsing that efficiently indexes strings and supports fast substring occurrence counting, with demonstrated experimental performance.

Contribution

It proposes a novel, succinct representation of ESP trees using bit strings and arrays, enabling efficient indexing and substring search in compressed space.

Findings

Uses (1+ε)n log n + 4n + o(n) bits for ESP representation.

Supports substring occurrence counting in O(1/ε)(m log n + occ_c log m log u) time.

Experimental results show competitive performance on benchmark datasets.

Abstract

Practical data structures for the edit-sensitive parsing (ESP) are proposed. Given a string S, its ESP tree is equivalent to a context-free grammar G generating just S, which is represented by a DAG. Using the succinct data structures for trees and permutations, G is decomposed to two LOUDS bit strings and single array in (1+\epsilon)n\log n+4n+o(n) bits for any 0<\epsilon <1 and the number n of variables in G. The time to count occurrences of P in S is in O(\frac{1}{\epsilon}(m\log n+occ_c(\log m\log u)), whereas m = |P|, u = |S|, and occ_c is the number of occurrences of a maximal common subtree in ESPs of P and S. The efficiency of the proposed index is evaluated by the experiments conducted on several benchmarks complying with the other compressed indexes.