  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 view the structured data source as meaningful
unstructured text, and then use the text to build an unsupervised neural
network model using a Natural Language Processing (NLP) technique called word
embedding. This model captures the hidden inter-/intra-column relationships
between database tokens of different types. For each database token, the model
includes a vector that encodes contextual semantic relationships. We seamlessly
integrate the word embedding model into existing SQL query infrastructure and
use it to enable a new class of SQL-based analytics queries called cognitive
intelligence (CI) queries. CI queries use the model vectors to enable complex
queries such as semantic matching, inductive reasoning queries such as
analogies, predictive queries using entities not present in a database, and,
more generally, using knowledge from external sources. We demonstrate unique
capabilities of Cognitive Databases using an Apache Spark based prototype to
execute inductive reasoning CI queries over a multi-modal database containing
text and images. We believe our first-of-a-kind system exemplifies using AI
functionality to endow relational databases with capabilities that were
previously very hard to realize in practice.
