$b$-Bit Sketch Trie: Scalable Similarity Search on Integer Sketches

TL;DR

This paper introduces a space-efficient trie data structure for scalable similarity search on integer sketches, significantly improving speed and memory efficiency over existing methods using real-world datasets.

Contribution

The paper proposes the $b$-bit sketch trie, a novel data structure leveraging succinct data structures for efficient similarity search on integer sketches.

Findings

Trie-based index improves search time by up to ten times.

Method reduces memory usage to 10 GiB on billion-scale datasets.

Experimental results outperform state-of-the-art methods in speed and space efficiency.

Abstract

Recently, randomly mapping vectorial data to strings of discrete symbols (i.e., sketches) for fast and space-efficient similarity searches has become popular. Such random mapping is called similarity-preserving hashing and approximates a similarity metric by using the Hamming distance. Although many efficient similarity searches have been proposed, most of them are designed for binary sketches. Similarity searches on integer sketches are in their infancy. In this paper, we present a novel space-efficient trie named $b$-bit sketch trie on integer sketches for scalable similarity searches by leveraging the idea behind succinct data structures (i.e., space-efficient data structures while supporting various data operations in the compressed format) and a favorable property of integer sketches as fixed-length strings. Our experimental results obtained using real-world datasets show that a trie-based index is built from integer sketches and efficiently performs similarity searches on the index by pruning useless portions of the search space, which greatly improves the search time and space-efficiency of the similarity search. The experimental results show that our similarity search is at most one order of magnitude faster than state-of-the-art similarity searches. Besides, our method needs only 10 GiB of memory on a billion-scale database, while state-of-the-art similarity searches need 29 GiB of memory.