02 January 2019
less than a minute read
This paper showed that the theory developed above, when combined with novel engineering techniques, can be used to build an effective prototype inDB ML system.
Authors: Maximilian Schleich, Dan Olteanu, Mahmoud Abo Khamis, Hung Q. Ngo, XuanLong Nguyen. 2019.
In Proceedings of the 2019 International Conference on Management of Data (SIGMOD ‘19).
This paper introduces LMFAO (Layered Multiple Functional Aggregate Optimization), an in-memory optimization and execution engine for batches of aggregates over the input database. The primary motivation for this work stems from the observation that for a variety of analytics over databases, their data-intensive tasks can be decomposed into group-by aggregates over the join of the input database relations. We exemplify the versatility and competitiveness of LMFAO for a handful of widely used analytics: learning ridge linear regression, classication trees, regression trees, and the structure of Bayesian networks using Chow-Liu trees; and data cubes used for exploration in data warehousing.
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