Similarity search through one-dimensional embeddings
Humberto Razente, Rafael L. Bernardes Lima, Maria Camila N. Barioni · 2017
The optimization of similarity queries is often done with specialized data structures known as metric access methods. It has recently been proposed the use of B+trees to index high dimensional data for range and nearest neighbor search in metric spaces. This work1 introduces a new access method called GroupSim and query algorithms for indexing and retrieving complex data by similarity. It employs a single B+tree in order to dynamically index data elements with regard to a set of one-dimensional embeddings. Our strategy uses a new scheme to store distance information, allowing to determine directly if each element lies on the intersection of the embeddings. We compare GroupSim with two related methods, iDistance and OmniB-Forest, and we show empirically the new access method outperforms them with regard to the time required to run similarity queries.