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Factorized Databases

Abstract:
This paper overviews factorized databases and their application to machine learning. The key observation underlying this work is that state-of-the-art relational query processing entails a high degree of redundancy in the computation and representation of query results. This redundancy can be avoided and is not necessary for subsequent analytics such as learning regression models.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1145/3003665.3003667

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Name:
European Research Council
Grant:
FADAMS 682588
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Name:
Google
Grant:
Faculty Research Award
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Name:
Amazon
Grant:
AWS Research Grant
Publisher:
Association for Computing Machinery
Journal:
SIGMOD Record More from this journal
Volume:
45
Issue:
2
Publication date:
2016-09-28
Acceptance date:
2016-06-15
DOI:
ISSN:
0163-5808
Pubs id:
pubs:629444
UUID:
uuid:9116c474-1e8a-4d63-943e-e0d038454f25
Local pid:
pubs:629444
Source identifiers:
629444
Deposit date:
2016-06-23

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