Speeding-up $q$-gram mining on grammar-based compressed texts

Abstract

We present an efficient algorithm for calculating $q$-gram frequencies on strings represented in compressed form, namely, as a straight line program (SLP). Given an SLP $\mathcal{T}$ of size $n$ that represents string $T$, the algorithm computes the occurrence frequencies of all $q$-grams in $T$, by reducing the problem to the weighted $q$-gram frequencies problem on a trie-like structure of size $m=|T|-dup(q,\mathcal{T})$, where $dup(q,\mathcal{T})$ is a quantity that represents the amount of redundancy that the SLP captures with respect to $q$-grams. The reduced problem can be solved in linear time. Since $m=O(qn)$, the running time of our algorithm is $O(\min \{|T|-dup(q,\mathcal{T}),qn\})$, improving our previous $O(qn)$ algorithm when $q=\Omega (|T|/n)$.

Cited by in corpus (1)

• Efficient LZ78 factorization of grammar compressed text