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Functional Aggregate Queries with Additive Inequalities

Functional Aggregate Queries with Additive Inequalities

This paper develops more formally the tensor-decomposition framework for semantic optimization.

Authors: Mahmoud Abo Khamis, Ryan R. Curtin, Benjamin Moseley, Hung Q. Ngo, Xuan Long Nguyen, Dan Olteanu, Maximilian Schleich. 2020.

In ACM Transactions on Database Systems (TODS ‘20). Vol. 45, No. 4, Article 17.

Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some of the input factors are defined by a collection of additive inequalities between variables. We refer to these queries as FAQ-AI for short. We present three applications of our FAQ-AI framework to relational machine learning: k-means clustering, training linear support vector machines, and training models using non-polynomial loss.

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