Name
Abstract | ||
|---|---|---|
Like most data models encountered in the Big Data ecosystem, RDF stores are managing large data sets by partitioning triples across a cluster of machines. Nevertheless, the graphical nature of RDF data as well as its associated SPARQL query execution model makes the efficient data distribution more involved than in other data models, e.g., relational. In this paper, we propose a novel system that is characterized by a trade-off between complexity of data partitioning and efficiency of query answering in cases where a query workload is known. The prototype is implemented over the Apache Spark framework, ensuring high availability, fault tolerance and scalability. This short paper presents the main features of the system and highlights the omnipresence of parallel computation across data fragmentation and allocation, encoding and query processing tasks. |
Year | Venue | Field |
|---|---|---|
2015 | International Semantic Web Conference (Posters & Demos) | Query optimization,Web search query,Data mining,RDF query language,Query expansion,Computer science,Sargable,Web query classification,SPARQL,RDF Schema,Database |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olivier Curé | 1 | 102 | 27.53 |
| Hubert Naacke | 2 | 128 | 25.41 |
| Mohamed Amine Baazizi | 3 | 9 | 1.53 |
| Bernd Amann | 4 | 425 | 59.99 |