\n","children":[{"type":"text","text":""}]},{"type":"p","children":[{"type":"text","text":"This paper develops more formally the tensor-decomposition framework for\nsemantic optimization.","italic":true}]},{"type":"p","children":[{"type":"text","text":"Authors: Mahmoud Abo Khamis, Ryan R. Curtin, Benjamin Moseley, Hung Q. Ngo, Xuan\nLong Nguyen, Dan Olteanu, Maximilian Schleich. 2020."}]},{"type":"p","children":[{"type":"text","text":"In ACM Transactions on Database Systems (TODS ‘20). Vol. 45, No. 4,\nArticle 17.","italic":true}]},{"type":"p","children":[{"type":"text","text":"Motivated by fundamental applications in databases and relational machine\nlearning, we formulate and study the problem of answering functional aggregate\nqueries (FAQ) in which some of the input factors are defined by a collection of\nadditive inequalities between variables. We refer to these queries as FAQ-AI for\nshort. We present three applications of our FAQ-AI framework to relational\nmachine learning: k-means clustering, training linear support vector machines,\nand training models using non-polynomial loss."}]},{"type":"p","children":[{"type":"text","text":"Read the PDF:\n"},{"type":"a","url":"https://arxiv.org/pdf/1812.09526.pdf","title":null,"children":[{"type":"text","text":"Functional Aggregate Queries with Additive Inequalities"}]}]}],"_content_source":{"queryId":"src/content/resources/functional-aggregate-queries-with-additive-inequalities.mdx","path":["resource","body"]}},"_content_source":{"queryId":"src/content/resources/functional-aggregate-queries-with-additive-inequalities.mdx","path":["resource"]}}},"errors":null,"query":"\n query resource($relativePath: String!) {\n resource(relativePath: $relativePath) {\n ... on Document {\n _sys {\n filename\n basename\n breadcrumbs\n path\n relativePath\n extension\n }\n id\n }\n ...ResourceParts\n }\n}\n \n fragment ResourceParts on Resource 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Functional Aggregate Queries with Additive Inequalities · RelationalAI
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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.