02 January 2019
less than a minute read
We consider the problem of incrementally maintaining the triangle count query under single-tuple updates to the input relations.
Authors: Ahmet Kara, Hung Q. Ngo, Milos Nikolic, Dan Olteanu, Haozhe Zhang. 2019.
In Proceedings of the 22nd International Conference on Database Theory (ICDT ‘19). (Best Paper Award).
We consider the problem of incrementally maintaining the triangle count query under single-tuple updates to the input relations. We introduce an approach that exhibits a space-time tradeoff such that the space-time product is quadratic in the size of the input database and the update time can be as low as the square root of this size. This lowest update time is worst-case optimal conditioned on the Online Matrix-Vector Multiplication conjecture.
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 More
This 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 More
This 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