A practical index for approximate dictionary matching with few mismatches

TL;DR

This paper introduces a simple, efficient split index for approximate dictionary matching with few mismatches, demonstrating competitive performance and effective compression, especially for Hamming distance queries.

Contribution

The paper presents a novel split index method for approximate dictionary matching that is simple, space-efficient, and performs well in practical scenarios.

Findings

Query times around 1 microsecond for one mismatch

The split index outperforms other algorithms for Hamming distance

Q-gram substitution reduces index size by up to 50%

Abstract

Approximate dictionary matching is a classic string matching problem (checking if a query string occurs in a collection of strings) with applications in, e.g., spellchecking, online catalogs, geolocation, and web searchers. We present a surprisingly simple solution called a split index, which is based on the Dirichlet principle, for matching a keyword with few mismatches, and experimentally show that it offers competitive space-time tradeoffs. Our implementation in the C++ language is focused mostly on data compaction, which is beneficial for the search speed (e.g., by being cache friendly). We compare our solution with other algorithms and we show that it performs better for the Hamming distance. Query times in the order of 1 microsecond were reported for one mismatch for the dictionary size of a few megabytes on a medium-end PC. We also demonstrate that a basic compression technique consisting in $q$-gram substitution can significantly reduce the index size (up to 50% of the input text size for the DNA), while still keeping the query time relatively low.