Streaming partitioning of RDF graphs for datalog reasoning
| Autor: | Boris Motik, Temitope Ajileye, Ian Horrocks |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | The Semantic Web ISBN: 9783030773847 ESWC |
| DOI: | 10.1007/978-3-030-77385-4_1 |
| Popis: | A cluster of servers is often used to reason over RDF graphs whose size exceeds the capacity of a single server. While many distributed approaches to reasoning have been proposed, the problem of data partitioning has received little attention thus far. In practice, data is usually partitioned by a variant of hashing, which is very simple, but it does not pay attention to data locality. Locality-aware partitioning approaches have been considered, but they usually process the entire dataset on a single server. In this paper, we present two new RDF partitioning strategies. Both are inspired by recent streaming graph partitioning algorithms, which partition a graph while keeping only a small subset of the graph in memory. We have evaluated our approaches empirically against hash and min-cut partitioning. Our results suggest that our approaches can significantly improve reasoning performance, but without unrealistic demands on the memory of the servers used for partitioning. |
| Databáze: | OpenAIRE |
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