Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Scalable semantic web data management using vertical partitioning
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
RDF-3X: a RISC-style engine for RDF
Proceedings of the VLDB Endowment
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
The RDF-3X engine for scalable management of RDF data
The VLDB Journal — The International Journal on Very Large Data Bases
LUBM: A benchmark for OWL knowledge base systems
Web Semantics: Science, Services and Agents on the World Wide Web
x-RDF-3X: fast querying, high update rates, and consistency for RDF databases
Proceedings of the VLDB Endowment
Editorial: Efficient incremental update and querying in AWETO RDF storage system
Data & Knowledge Engineering
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With the fast growth of the knowledge bases built over the Internet, storing and querying millions or billions of RDF triples in a knowledge base have attracted increasing research interests. Although the latest RDF storage systems achieve good querying performance, few of them pay much attention to the characteristic of dynamic growth of the knowledge base. In this paper, to consider the efficiency of both querying and incremental update in RDF data, we propose a hAsh-based tWo-tiEr rdf sTOrage system (abbr. to AWETO) with new index architecture and query execution engine. The performance of our system is systematically measured over two large-scale datesets. Compared with the other three state-of-the-art RDF storage systems, our system achieves the best incremental update efficiency, meanwhile, the query efficiency is competitive.