S-RDF: A New RDF Serialization Format for Better Storage Without Losing Human Readability

International audienceNowadays, RDF data becomes more and more popular on the Web due to the advances of the Semantic Web and the Linked Open Data initiatives. Several works are focused on transforming relational databases to RDF by storing related data in N-Triple serialization format. However, these approaches do not take into account the existing normalization of their databases since N-Triple format allows data redundancy and does not control any normalization by itself. Moreover, the mostly used and recommended serialization formats, such as RDF/XML, Turtle, and HDT, have either high human-readability but waste storage capacity, or focus further on storage capacities while providing low human-readability. To overcome these limitations, we propose here a new serialization format, called S-RDF. By considering the structure (graph) and values of the RDF data separately, S-RDF reduces the duplicity of values by using unique identifiers. Results show an important improvement over the existing serialization formats in terms of storage (up to 71,66% w.r.t. N-Triples) and human readability

Exploiting emergent schemas to make RDF systems more efficient

We build on our earlier finding that more than 95 % of the triples in actual RDF triple graphs have a remarkably tabular structure, whose schema does not necessarily follow from explicit metadata such as ontologies, but for which an RDF store can automatically derive by looking at the data using so-called “emergent schema” detection techniques. In this paper we investigate how computers and in particular RDF stores can take advantage from this emergent schema to more compactly store RDF data and more efficiently optimize and execute SPARQL queries. To this end, we contribute techniques for efficient emergent schema aware RDF storage and new query operator algorithms for emergent schema aware scans and joins. In all, these techniques allow RDF schema processors fully catch up with relational database techniques in terms of rich physical database design options and efficiency, without requiring a rigid upfront schema structure definition

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them