Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Optimized Index Structures for Querying RDF from the Web
LA-WEB '05 Proceedings of the Third Latin American Web Congress
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
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
Hexastore: sextuple indexing for semantic web data management
Proceedings of the VLDB Endowment
Column-store support for RDF data management: not all swans are white
Proceedings of the VLDB Endowment
SP^2Bench: A SPARQL Performance Benchmark
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Scalable join processing on very large RDF graphs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Scalable indexing of RDF graphs for efficient join processing
Proceedings of the 18th ACM conference on Information and knowledge management
The RDF-3X engine for scalable management of RDF data
The VLDB Journal — The International Journal on Very Large Data Bases
FlexTable: using a dynamic relation model to store RDF data
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Tridex: A lightweight triple index for relational database-based Semantic Web data management
Expert Systems with Applications: An International Journal
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RDF has gained great interest in both academia and industry as an important language to describe graph data. Several approaches have been proposed for storing and querying RDF data efficiently; each works best under certain circumstances, e.g. certain types of data and/or queries. However, there was lack of a thorough understanding of exactly what these circumstances are, as different data-sets and query sets are used in the empirical evaluations in the literature to highlight their proposed techniques. In this work, we capture the characteristics of data and queries that are critical to the RDF storage and query evaluation efficiency and provide a thorough analysis of the existing storage, indexing and query evaluation techniques based on these characteristics. We believe that our study not only can be used in evaluating both existing and emerging RDF data management techniques, but also lays the foundations for designing RDF benchmarks for more in-depth performance analysis of RDF data management systems.