Parallel materialisation of datalog programs in centralised, main-memory RDF systems
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:
- Peer review status:
- Peer reviewed
- 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
- Publication date:
- Copyright holder:
- Association for the Advancement of Artificial Intelligence
- Copyright date:
- 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.
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record