Book section
Parallel materialisation of datalog programs in centralised, main-memory RDF systems
- Abstract:
-
We present a novel approach to parallel materialisation (i.e., fixpoint computation) of datalog programs in centralised, main-memory, multi-core RDF systems. Our approach comprises an algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, ‘mostly’ lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well: with 16 physical cores, materialisation can be up to 13.9 times faster than with just one core.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Nenov, Y
Piro, R
Grant:
EP/J020214/1
EP/L012138/1
+ Engineering and Physical Sciences research Council
More from this funder
Funding agency for:
Motik, B
Grant:
EP/K00607X/1
Bibliographic Details
- Publisher:
- Association for the Advancement of Artificial intelligence Publisher's website
- Host title:
- Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence and the Twenty-Sixth Innovative Applications of Artificial Intelligence Conference
- Volume:
- 1
- Publication date:
- 2014-01-01
- ISSN:
-
2159-5399
- ISBN:
- 9781577356615
Item Description
- Language:
- English
- Keywords:
- Subjects:
- UUID:
-
uuid:5800d74f-b9f6-4b12-9908-a45be70b29d2
- Local pid:
- ora:9552
- Deposit date:
- 2014-12-08
Related Items
Terms of use
- Copyright holder:
- Association for the Advancement of Artificial Intelligence
- Copyright date:
- 2014
- Notes:
- The full-text of this paper is not currently available in ORA, but you may be able to access the paper via the publisher copy link on this record page. Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
- Licence:
- CC Attribution (CC BY)
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