D-Hive: Data Bees Pollinating RDF, Text, and Time

TLDR: D-Hive is put forward, a system facilitating analytics over RDF-style (SPO) triples augmented with text and (validity / transaction) time capable of addressing the functionality and scalability requirements which current solutions cannot meet.

Abstract: Although the problem of integrating IR and DB solutions is considered “old”, the increasing importance of big data analytics and its formidable demands for both enriched functionality and scalable performance creates the need to revisit the problem itself and to see possible solutions from a new perspective. Our goal is to develop a system that will make large corpora aware of entities and relationships (ER), addressing the challenges in searching and analyzing ER patterns in web data and social media. We put forward D-Hive, a system facilitating analytics over RDF-style (SPO) triples augmented with text and (validity / transaction) time capable of addressing the functionality and scalability requirements which current solutions cannot meet. We consider various alternatives for the data modeling, storage, indexing, and query processing engines of D-Hive paying attention to the challenges that must be met, which include i) scalable joint indexing of SPO-text-time tuples (quads, quints, octs, etc.), ii) efficient processing of complex queries that involve RDF star and path joins, filtering and grouping on text phrases, band joins over time, and more, as well as iii) optimizing the execution plans for such analytics.