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Learning linear regression models over factorized joins

Abstract:

We investigate the problem of building least squares regression models over training datasets defined by arbitrary join queries on database tables. Our key observation is that joins entail a high degree of redundancy in both computation and data representation, which is not required for the end-to-end solution to learning over joins. We propose a new paradigm for computing batch gradient descent that exploits the factorized computation and representation of the training datasets, a rewriting ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1145/2882903.2882939

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
St Cross College
Role:
Author
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Grant:
Google Research Faculty award
Publisher:
Association for Computing Machinery Publisher's website
Journal:
SIGMOD '16 Proceedings of the 2016 International Conference on Management of Data Journal website
Volume:
26-June-2016
Pages:
3-18
Host title:
SIGMOD '16 Proceedings of the 2016 International Conference on Management of Data
Publication date:
2016-06-26
Acceptance date:
2016-03-04
Event location:
San Francisco
Event start date:
2016-06-26T00:00:00Z
Event end date:
2016-07-01T00:00:00Z
DOI:
ISSN:
0730-8078
Source identifiers:
609175
ISBN:
9781450335317
Pubs id:
pubs:609175
UUID:
uuid:c9a934ce-9516-4751-87d4-857cdac36284
Local pid:
pubs:609175
Deposit date:
2018-01-10

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