Binary Jumbled Indexing: Suffix tree histogram

TL;DR

This paper investigates the Binary Jumbled Indexing Problem, analyzing average-case complexity, and introduces a suffix tree-based algorithm that improves practical performance despite similar theoretical complexity.

Contribution

It presents a new suffix tree-based algorithm, SFTree, which reduces memory access overhead and improves practical performance for binary jumbled indexing.

Findings

Average number of runs is n/4, confirming quadratic worst-case behavior.

SFTree outperforms existing algorithms in practical scenarios.

Both algorithms have similar theoretical complexity, but SFTree is more efficient in practice.

Abstract

Given a binary string $\omega$ over the alphabet $\{0, 1\}$, a vector $(a, b)$ is a Parikh vector if and only if a factor of $\omega$ contains exactly $a$ occurrences of $0$ and $b$ occurrences of $1$. Answering whether a vector is a Parikh vector of $\omega$ is known as the Binary Jumbled Indexing Problem (BJPMP) or the Histogram Indexing Problem. Most solutions to this problem rely on an $O(n)$ word-space index to answer queries in constant time, encoding the Parikh set of $\omega$, i.e., all its Parikh vectors. Cunha et al. (Combinatorial Pattern Matching, 2017) introduced an algorithm (JBM2017), which computes the index table in $O(n+\rho^2)$ time, where $\rho$ is the number of runs of identical digits in $\omega$, leading to $O(n^2)$ in the worst case. We prove that the average number of runs $\rho$ is $n/4$, confirming the quadratic behavior also in the average-case. We propose a new algorithm, SFTree, which uses a suffix tree to remove duplicate substrings. Although SFTree also has an average-case complexity of $\Theta(n^2)$ due to the fundamental reliance on run boundaries, it achieves practical improvements by minimizing memory access overhead through vectorization. The suffix tree further allows distinct substrings to be processed efficiently, reducing the effective cost of memory access. As a result, while both algorithms exhibit similar theoretical growth, SFTree significantly outperforms others in practice. Our analysis highlights both the theoretical and practical benefits of the SFTree approach, with potential extensions to other applications of suffix trees.