In-database learning with sparse tensors

Conference item

In-database analytics is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features, export the data, convert data format, train models using an external tool, reimport the parameters. It is also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This paper introduces a unified framework for training and evaluating a class of statistical learning models inside a relational database. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from relational database theory such as schema information, query structure, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database learning problems and design efficient algorithms to solve them. The algorithms and models proposed in the paper have already been implemented inside the LogicBlox database engine and used in retail-planning and forecasting applications, with significant performance benefits over out-of-database solutions that require the costly data-export loop.

Authors: Abo Khamis, M; Ngo, H; Nguyen, X; Olteanu, D; Schleich, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Association for Computing Machinery
• Host title: SIGMOD/PODS '18 Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
• Journal: ACM Principles of Database Systems
• Pages: 325-340
• Publication date: 2018-05-27
• Acceptance date: 2017-09-01
• Event location: Houston
• DOI: 10.1145/3196959.3196960
• ISBN: 9781450347068
• Keywords: tensors; model reparameterization; functional dependencies; in-database analytics; functional aggregate queries