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,...
| Автори: | , , , , |
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| Формат: | Conference item |
| Мова: | English |
| Опубліковано: |
Association for the Advancement of Artificial intelligence
2014
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| Предмети: |
| _version_ | 1826317506737864704 |
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| author | Motik, B Nenov, Y Piro, R Horrocks, I Olteanu, D |
| author_facet | Motik, B Nenov, Y Piro, R Horrocks, I Olteanu, D |
| author_sort | Motik, B |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T22:30:07Z |
| format | Conference item |
| id | oxford-uuid:5800d74f-b9f6-4b12-9908-a45be70b29d2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:40:29Z |
| publishDate | 2014 |
| publisher | Association for the Advancement of Artificial intelligence |
| record_format | dspace |
| spelling | oxford-uuid:5800d74f-b9f6-4b12-9908-a45be70b29d22025-02-18T15:18:03ZParallel materialisation of datalog programs in centralised, main-memory RDF systemsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:5800d74f-b9f6-4b12-9908-a45be70b29d2Artificial IntelligenceDatabasesKnowledge Representation and ReasoningComputer ScienceEnglishOxford University Research Archive - ValetAssociation for the Advancement of Artificial intelligence2014Motik, BNenov, YPiro, RHorrocks, IOlteanu, DWe 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. |
| spellingShingle | Artificial Intelligence Databases Knowledge Representation and Reasoning Computer Science Motik, B Nenov, Y Piro, R Horrocks, I Olteanu, D Parallel materialisation of datalog programs in centralised, main-memory RDF systems |
| title | Parallel materialisation of datalog programs in centralised, main-memory RDF systems |
| title_full | Parallel materialisation of datalog programs in centralised, main-memory RDF systems |
| title_fullStr | Parallel materialisation of datalog programs in centralised, main-memory RDF systems |
| title_full_unstemmed | Parallel materialisation of datalog programs in centralised, main-memory RDF systems |
| title_short | Parallel materialisation of datalog programs in centralised, main-memory RDF systems |
| title_sort | parallel materialisation of datalog programs in centralised main memory rdf systems |
| topic | Artificial Intelligence Databases Knowledge Representation and Reasoning Computer Science |
| work_keys_str_mv | AT motikb parallelmaterialisationofdatalogprogramsincentralisedmainmemoryrdfsystems AT nenovy parallelmaterialisationofdatalogprogramsincentralisedmainmemoryrdfsystems AT piror parallelmaterialisationofdatalogprogramsincentralisedmainmemoryrdfsystems AT horrocksi parallelmaterialisationofdatalogprogramsincentralisedmainmemoryrdfsystems AT olteanud parallelmaterialisationofdatalogprogramsincentralisedmainmemoryrdfsystems |