RelationalAI
01 January 2017
less than a minute read
This paper connects semantic query optimization, physical query optimization & cost estimation, to information theory with provable bounds.
Authors: Mahmoud Abo Khamis, Hung Q. Ngo, Dan Suciu. 2017.
In Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS ‘17) (Invited to the Journal of the ACM)
Recent works on bounding the output size of a conjunctive query with functional dependencies and degree bounds have shown a deep connection between fundamental questions in information theory and database theory. We prove analogous output bounds for disjunctive datalog rules, and answer
several open questions regarding the tightness and looseness of these bounds along the way. The bounds are intimately related to Shannon-type information inequalities. We devise the notion of a “proof sequence” of a specific class of
Shannon-type information inequalities called “Shannon flow inequalities”. We then show how a proof sequence can be used as symbolic instructions to guide an algorithm called PANDA, which answers disjunctive datalog rules within the size bound predicted. We show that PANDA can be used as a black-box to devise algorithms matching precisely the fractional hypertree width and the submodular width run-times for aggregate and conjunctive queries with functional dependencies and degree bounds.
Molham shares some history of relational databases, trends in modern cloud-native database systems, and the innovations pioneered at RelationalAI to bring deep learning with relations from idea to reality.
Read MoreThis incredible panel of experts gathered to discuss the current state of AI and machine learning workloads inside databases. The panel discussed new techniques, technologies, and recent papers that progress our understanding of what is possible. Q&A among the panel and from the audience concludes this deep and wide ranging conversation.
Read MoreThis talk explores several techniques to improve the runtime performance of machine learning by taking advantage of the underlying structure of relational data. While most data scientists use relational data in their work, the data science tooling that works with relational data is quite lacking today. Let’s explore these new techniques and see how we can drastically improve machine learning through a database-oriented lens.
Read More