A succinct data structure for self-indexing ternary relations

The final publication is available via http://dx.doi.org/10.1016/j.jda.2016.10.002[Abstract] The representation of binary relations has been intensively studied and many different theoretical and practical representations have been proposed to answer the usual queries in multiple domains. However, ternary relations have not received as much attention, even though many real-world applications require the processing of ternary relations. In this paper we present a new compressed and self-indexed data structure that we call Interleaved K2-tree (IK2-tree), designed to compactly represent and efficiently query general ternary relations. The IK2-tree is an evolution of an existing data structure, the K2-tree [6], initially designed to represent Web graphs and later applied to other domains. The IK2-tree is able to extend the K2-tree to represent a ternary relation, based on the idea of decomposing it into a collection of binary relations but providing indexing capabilities in all the three dimensions. We present different ways to use IK2-tree to model different types of ternary relations using as reference two typical domains: RDF and Temporal Graphs. We also experimentally evaluate our representations comparing them in space usage and performance with other solutions of the state of the art.Ministerio de Economía y Competitividad; TIN2013-46238-C4-3-RXunta de Galicia; GRC2013/053Chile. Núcleo Milenio Información y Coordinación en Redes; ICM/FIC RC130003Chile.Fondo Nacional de Desarrollo Científico y Tecnológico; 1-14079

Towards Making Distributed RDF processing FLINker

In the last decade, the Resource Description Framework (RDF) has become the de-facto standard for publishing semantic data on the Web. This steady adoption has led to a significant increase in the number and volume of available RDF datasets, exceeding the capabilities of traditional RDF stores. This scenario has introduced severe big semantic data challenges when it comes to managing and querying RDF data at Web scale. Despite the existence of various off-the-shelf Big Data platforms, processing RDF in a distributed environment remains a significant challenge. In this position paper, based on an indepth analysis of the state of the art, we propose to manage large RDF datasets in Flink, a well-known scalable distributed Big Data processing framework. Our approach, which we refer to as FLINKer extends the native graph abstraction of Flink, called Gelly, with RDF graph and SPARQL query processing capabilities