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Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema
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P-Grid: a self-organizing structured P2P system
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C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
An efficient SQL-based RDF querying scheme
VLDB '05 Proceedings of the 31st international conference on Very large data bases
An Evaluation of RDF Storage Systems for Large Data Applications
SKG '05 Proceedings of the First International Conference on Semantics, Knowledge and Grid
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GridVine: An Infrastructure for Peer Information Management
IEEE Internet Computing
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
RDF-3X: a RISC-style engine for RDF
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Hexastore: sextuple indexing for semantic web data management
Proceedings of the VLDB Endowment
Efficient Indices Using Graph Partitioning in RDF Triple Stores
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
A demonstration of SciDB: a science-oriented DBMS
Proceedings of the VLDB Endowment
LUBM: A benchmark for OWL knowledge base systems
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HYRISE: a main memory hybrid storage engine
Proceedings of the VLDB Endowment
gStore: answering SPARQL queries via subgraph matching
Proceedings of the VLDB Endowment
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A structural approach to indexing triples
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TripleProv: efficient processing of lineage queries in a native RDF store
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Semantic Web - Linked Data for science and education
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The proliferation of semantic data on the Web requires RDF database systems to constantly improve their scalability and transactional efficiency. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. This paper introduces a novel database system for RDF data management called dipLODocus[RDF], which supports both transactional and analytical queries efficiently. dipLODocus[RDF] takes advantage of a new hybrid storage model for RDF data based on recurring graph patterns. In this paper, we describe the general architecture of our system and compare its performance to state-of-the-art solutions for both transactional and analytic workloads.