Hierarchical Locality Sensitive Hashing for Structured Data: A Survey

TL;DR

This survey reviews hierarchical Locality Sensitive Hashing techniques designed to efficiently compute similarities in structured data like sequences, trees, and graphs, highlighting recent progress, applications, and future challenges.

Contribution

It provides a comprehensive overview of hierarchical LSH algorithms for structured data, connecting different data structures and application scenarios, and discusses future research challenges.

Findings

Hierarchical LSH algorithms effectively preserve structure information.

Various applications benefit from hierarchical LSH in structured data analysis.

Identifies key challenges and future directions in hierarchical LSH research.

Abstract

Data similarity (or distance) computation is a fundamental research topic which fosters a variety of similarity-based machine learning and data mining applications. In big data analytics, it is impractical to compute the exact similarity of data instances due to high computational cost. To this end, the Locality Sensitive Hashing (LSH) technique has been proposed to provide accurate estimators for various similarity measures between sets or vectors in an efficient manner without the learning process. Structured data (e.g., sequences, trees and graphs), which are composed of elements and relations between the elements, are commonly seen in the real world, but the traditional LSH algorithms cannot preserve the structure information represented as relations between elements. In order to conquer the issue, researchers have been devoted to the family of the hierarchical LSH algorithms. In this paper, we explore the present progress of the research into hierarchical LSH from the following perspectives: 1) Data structures, where we review various hierarchical LSH algorithms for three typical data structures and uncover their inherent connections; 2) Applications, where we review the hierarchical LSH algorithms in multiple application scenarios; 3) Challenges, where we discuss some potential challenges as future directions.