Add to ORKGOne approach to leverage scalable systems for RDF manage-ment is partitioning large datasets across distributed servers. In this paper we consider workload data, given in the form of query patterns and their frequencies, for determining how to partition RDF datasets. Our experimental study shows that our workload-aware method is an effective way to clus-ter related data and provides better query response times compared to an elementary fragmentation method
International audienceLike most data models encountered in the Big Data ecosystem, RDF stores are ma...
Evaluating joins over RDF data stored in a shared-nothing server cluster is key to processing truly ...
The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic ...
The growing popularity of Resource Description Framework (RDF) as a mode for data exchange and integ...
A common approach to processing large RDF datasets is to partition the data in a cluster of shared-n...
Many RDF systems support reasoning with Datalog rules via materialisation, where all conclusions of ...
The Resource Description Framework (RDF) is a World Wide Web Consortium (W3C) standard for the conce...
Abstract—The emerging need for conducting complex analysis over big RDF datasets calls for scale-out...
Massive volumes of big RDF data are growing beyond the performance capacity of conventional RDF data...
Web-scale RDF datasets are increasingly processed using distributed RDF data stores built on top of ...
Abstract. Web-scale RDF datasets are increasingly processed using dis-tributed RDF data stores built...
When RDF datasets become too large to be managed by centralised systems, they are often distributed ...
To simplify data integration and exchange, modern applications often represent their data using the...
The Semantic Web, or the Web of Data, promotes common data formats for representing structured data ...
The generation of RDF data has accelerated to the point where many data sets need to be partitioned ...
International audienceLike most data models encountered in the Big Data ecosystem, RDF stores are ma...
Evaluating joins over RDF data stored in a shared-nothing server cluster is key to processing truly ...
The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic ...
The growing popularity of Resource Description Framework (RDF) as a mode for data exchange and integ...
A common approach to processing large RDF datasets is to partition the data in a cluster of shared-n...
Many RDF systems support reasoning with Datalog rules via materialisation, where all conclusions of ...
The Resource Description Framework (RDF) is a World Wide Web Consortium (W3C) standard for the conce...
Abstract—The emerging need for conducting complex analysis over big RDF datasets calls for scale-out...
Massive volumes of big RDF data are growing beyond the performance capacity of conventional RDF data...
Web-scale RDF datasets are increasingly processed using distributed RDF data stores built on top of ...
Abstract. Web-scale RDF datasets are increasingly processed using dis-tributed RDF data stores built...
When RDF datasets become too large to be managed by centralised systems, they are often distributed ...
To simplify data integration and exchange, modern applications often represent their data using the...
The Semantic Web, or the Web of Data, promotes common data formats for representing structured data ...
The generation of RDF data has accelerated to the point where many data sets need to be partitioned ...
International audienceLike most data models encountered in the Big Data ecosystem, RDF stores are ma...
Evaluating joins over RDF data stored in a shared-nothing server cluster is key to processing truly ...
The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic ...