An efficient SQL-based RDF querying scheme
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
Graph Theory
SW-Store: a vertically partitioned DBMS for Semantic Web data management
The VLDB Journal — The International Journal on Very Large Data Bases
Scalable join processing on very large RDF graphs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Semantics and complexity of SPARQL
ACM Transactions on Database Systems (TODS)
The RDF-3X engine for scalable management of RDF data
The VLDB Journal — The International Journal on Very Large Data Bases
Foundations of SPARQL query optimization
Proceedings of the 13th International Conference on Database Theory
x-RDF-3X: fast querying, high update rates, and consistency for RDF databases
Proceedings of the VLDB Endowment
RFID data analysis using tensor calculus for supply chain management
Proceedings of the 20th ACM international conference on Information and knowledge management
Efficiently joining group patterns in SPARQL queries
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
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We are witnessing the evolution of the Web from a worldwide information space of linked documents to a global knowledge base, composed of semantically interconnected resources (to date, 25 billion RDF triples, interlinked by around 395 million RDF links). RDF comes equipped with the SPARQL language for querying data in RDF format. Although many aspects of the challenges faced in large-scale RDF data management have already been studied in the database research community, current approaches provide centralized hard-coded solutions, with high consumption of resources; moreover, these exhibit very limited flexibility dealing with queries, at various levels of granularity and complexity (e.g. so-called non-conjunctive queries that use SPARQL's union or optional). In this paper we propose a general model for answering SPARQL queries based on the first principles of linear algebra, in particular on tensorial calculus. Leveraging our abstract algebraic framework, our technique allows both quick decentralized processing, and centralized massive analysis. Experimental results show that our approach, utilizing recent linear algebra techniques--tailored to performance and accuracy as required in applied mathematics and physics fields--can process analysis efficiently, when compared to competitors.