01 January 2018
less than a minute read
This paper aims to be a brief introduction to the design and analysis of worst-case optimal join algorithms. We discuss the key techniques for proving runtime and output size bounds.
Author: Hung Q. Ngo. 2018.
In Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (SIGMOD/PODS ‘18).
Worst-case optimal join algorithms are the class of join algorithms whose runtime match the worst-case output size of a given join query. While the first provably worst-case optimal join algorithm was discovered relatively recently, the techniques and results surrounding these algorithms grow out of decades
of research from a wide range of areas, intimately connecting graph theory, algorithms, information theory, constraint satisfaction, database theory, and geometric inequalities. These ideas are not just paperware: in addition to academic project implementations, two variations of such algorithms are the work-horse join algorithms of commercial database and data analytics engines.
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