  Finding nearest neighbors in high-dimensional spaces is a fundamental
operation in many diverse application domains. Locality Sensitive Hashing (LSH)
is one of the most popular techniques for finding approximate nearest neighbor
searches in high-dimensional spaces. The main benefits of LSH are its
sub-linear query performance and theoretical guarantees on the query accuracy.
In this survey paper, we provide a review of state-of-the-art LSH and
Distributed LSH techniques. Most importantly, unlike any other prior survey, we
present how Locality Sensitive Hashing is utilized in different application
domains.
