Much work has been devoted to supporting RDF data. But state-of-the-art systems and
methods still cannot handle web scale RDF data effectively. Furthermore, many useful and …

The generation of RDF data has accelerated to the point where many data sets need to be
partitioned across multiple machines in order to achieve reasonable performance when …

We propose techniques for processing SPARQL queries over a large RDF graph in a
distributed environment. We adopt a “partial evaluation and assembly” framework …

RDF has seen increased adoption in recent years, prompting the standardization of the
SPARQL query language for RDF, and the development of local and distributed engines for …

We address efficient processing of SPARQL queries over RDF datasets. The proposed
techniques, incorporated into the gStore system, handle, in a uniform and scalable manner …

Integrated access to multiple distributed and autonomous RDF data sources is a key
challenge for many semantic web applications. As a reaction to this challenge, SPARQL, the …

RDF has become very popular for semantic data publishing due to its flexible and universal
graph-like data model. Yet, the ever-increasing size of RDF data collections makes it more …

Distributed SPARQL engines promise to support very large RDF datasets by utilizing shared-
nothing computer clusters. Some are based on distributed frameworks such as MapReduce; …

Due to the increasing use of RDF data, efficient processing of SPARQL queries over RDF
datasets has become an important issue. However, existing solutions suffer from two …

The Semantic Web is the initiative of the W3C to make information on the Web readable not
only by humans but also by machines. RDF is the data model for Semantic Web data, and …