An Adaptive Multi-Level Hashing Structure for Fast Approximate Similarity Search
Fast information retrieval is an essential task in data management, mainly due to the increasing availability of data. To address this problem, database researchers have developed indexing techniques to logically organize elements from large datasets in order to answer queries efficiently. In this context, an approximate similarity search algorithm known as Locality Sensitive Hashing (LSH) was recently proposed to query high-dimensional datasets with efficient computational time. The query cost of LSH is sub-linear on the dataset size.