19 citations · 34 across the 6 of their papers we have counts for
6 papers
B+ANN: A Fast Billion-Scale Disk-based Nearest-Neighbor Index
Selim Furkan Tekin, Rajesh Bordawekar
Storing and processing of embedding vectors by specialized Vector databases (VDBs) has become the linchpin in building modern AI pipelines. Most current VDBs employ variants of a g…
A Scalable Space-efficient In-database Interpretability Framework for Embedding-based Semantic SQL Queries
Prabhakar Kudva, Rajesh Bordawekar, Apoorva Nitsure
AI-Powered database (AI-DB) is a novel relational database system that uses a self-supervised neural network, database embedding, to enable semantic SQL queries on relational table…
EFloat: Entropy-coded Floating Point Format for Compressing Vector Embedding Models
Rajesh Bordawekar, Bulent Abali, Ming-Hung Chen
In a large class of deep learning models, including vector embedding models such as word and database embeddings, we observe that floating point exponent values cluster around a fe…
Unlocking New York City Crime Insights using Relational Database Embeddings
Apoorva Nitsure, Rajesh Bordawekar, Jose Neves
This version withdrawn by arXiv administrators because the author did not have the right to agree to our license at the time of submission.
Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities
Rajesh Bordawekar, Bortik Bandyopadhyay, Oded Shmueli
We propose Cognitive Databases, an approach for transparently enabling Artificial Intelligence (AI) capabilities in relational databases. A novel aspect of our design is to first v…
Enabling Cognitive Intelligence Queries in Relational Databases using Low-dimensional Word Embeddings
Rajesh Bordawekar, Oded Shmueli
We apply distributed language embedding methods from Natural Language Processing to assign a vector to each database entity associated token (for example, a token may be a word occ…