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Incremental update of datalog materialisation: the backward/forward algorithm

Abstract:

Datalog-based systems often materialise all consequences of a datalog program and the data, allowing users' queries to be evaluated directly in the materialisation. This process, however, can be computationally intensive, so most systems update the materialisation incrementally when input data changes. We argue that existing solutions, such as the well-known Delete/Rederive (DRed) algorithm, can be inefficient in cases when facts have many alternate derivations. As a possible remedy, we pr...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author

Contributors

Role:
Editor
Role:
Editor
Engineering and Physical Sciences Research Council More from this funder
European Union More from this funder
Publisher:
AAAI Press Publisher's website
Pages:
1560-1568
Host title:
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence and the Twenty-Seventh Innovative Applications of Artificial Intelligence Conference, January 25–30, 2015, Austin, Texas, USA
Publication date:
2015-01-01
EISSN:
2374-3468
ISSN:
2159-5399
Source identifiers:
576350
ISBN:
9781577356981
Keywords:
Pubs id:
pubs:576350
UUID:
uuid:6d5a123b-4934-41da-8f88-d9593414531f
Local pid:
pubs:576350
Deposit date:
2016-03-06

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