RDF, a simple yet expressive data model, is widely recognised as the foundation for the Semantic Web. We revisit the RDF specification and analyse five problems: literals are not addressable, blank nodes are not addressable, reification is not semantically recognised, language tags are not addressable, and language tags can not be combined. We introduce SiRDF, a simplified data model with fewer...

The basic data model for the Semantic Web is the Resource Description Framework (RDF). From a data management point of view, it resembles a lightweight knowledge base. In this paper we address updates in RDF. It is known that the semantics of updates for data models becomes unclear when the model turns, even slightly, more general than a simple relational structure. Viewing RDF as a knowledge b...

The proliferation of semantic data on the Web requires RDF database systems to constantly improve their scalability and transactional efficiency. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. This paper introduces a novel database system for RDF data management called dipLODocus[RDF] ,...

The Resource Description Framework (RDF) is a language for metadata assertions about information resources on the World-Wide Web, and is thus a foundation for a future Semantic Web. The atomic construct of RDF are statements, which are triples consisting of the resource being described, a property, and a property value. A collection of RDF statements can be intuitively understood as a graph: re...

The ability of the RDF data model to link data from heterogeneous domains has led to an explosive growth of RDF data. So, evaluating SPARQL queries over large RDF data has been crucial for the semantic web community. However, due to the graph nature of RDF data, evaluating SPARQL queries in relational databases and common data-parallel systems needs a lot of joins and is inefficient. On the oth...

With the success of the semantic web infrastructures for storing and querying RDF data are gaining importance. A couple of systems are available now that provide basic database functionality for RDF data. Compared to modern database systems, RDF storage technology still lacks sophisticated optimization methods for query processing. Current work in this direction is mainly focussed on index stru...

This document defines extensions of the RDF data model and of the SPARQL query language that capture an alternative approach to represent statement-level metadata. While this alternative approach is backwards compatible with RDF reification as defined by the RDF standard, the approach aims to address usability and data management shortcomings of RDF reification. One of the great advantages of t...

Research in data warehousing (DW) has developed expressive and efficient tools for the multidimensional analysis of large amounts of data. As more data gets produced and shared in RDF, analytic concepts and tools for analyzing such irregular, graph-shaped, semantic-rich data need to be revisited. We introduce the first all-RDF model for warehousing RDF graphs. Notably, we define analytical sche...

Data Cube represents one of the basic means for storing, processing and analyzing statistical data. Recently, the RDF Data Cube Vocabulary became a W3C recommendation and at the same time interesting datasets using it started to appear. Along with them appeared the need for compatible visualization tools. The Linked Data Visualisation Model is a formalism focused on this area and is implemented...