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P. Boncz, S. Manegold, M. Kersten (1999)
Database Architecture Optimized for the New Bottleneck: Memory Access
Cathrin Weiss, Panagiotis Karras, A. Bernstein (2008)
Hexastore: sextuple indexing for semantic web data managementProc. VLDB Endow., 1
J. Hellerstein, J. Naughton, A. Pfeffer (1995)
Generalized Search Trees for Database Systems
E. Bertino, Won Kim (1989)
Indexing Techniques for Queries on Nested ObjectsIEEE Trans. Knowl. Data Eng., 1
E. Chong, Souripriya Das, G. Eadon, Jagannathan Srinivasan (2005)
An Efficient SQL-based RDF Querying Scheme
The semantic web index
A. Ailamaki, D. DeWitt, M. Hill, Marios Skounakis (2001)
Weaving Relations for Cache Performance
P. Boncz, M. Zukowski, N. Nes (2005)
MonetDB/X100: Hyper-Pipelining Query Execution
D. Abadi, Adam Marcus, S. Madden, Katherine Hollenbach (2007)
Using The Barton Libraries Dataset As An RDF benchmark
Kamil Bajda-Pawlikowski
Querying Rdf Data Stored in Dbms: Sparql to Sql Conversion
S. Alexaki, V. Christophides, G. Karvounarakis, D. Plexousakis, Karsten Tolle (2001)
The ICS-FORTH RDFSuite: Managing Voluminous RDF Description Bases
K. Wilkinson (2006)
Jena Property Table Implementation
R. MacNicol, Blaine French (2004)
Sybase IQ Multiplex - Designed For Analytics
R. Agrawal, Amit Somani, Yirong Xu (2001)
Storage and Querying of E-Commerce Data
(2004)
RDF Primer. W3C Recommendation. http://www.w3.org/TR/rdf- primer (2004) 39. RDQL—A Query Language for RDF. W3C Member Submission
Stephen Harris, Nicholas Gibbins (2003)
3store: Efficient Bulk RDF Storage
Dynamic Tables: An Architecture for Managing Evolving, Heterogeneous Biomedical Data in Relational Database Management Systems
Jing Lu, Feng Cao, Li Ma, Yong Yu, Yue Pan (2008)
An Effective SPARQL Support over Relational Databases
P. Boncz, M. Kersten (1999)
MIL primitives for querying a fragmented worldThe VLDB Journal, 8
AND A.obj = "
D. Abadi, S. Madden, N. Hachem (2008)
Column-stores vs. row-stores: how different are they really?
D. Abadi (2008)
Query execution in column-oriented database systems
D. Abadi (2007)
Column Stores for Wide and Sparse Data
(2007)
Performance and scalability evaluation of practical ontology systems
(2006)
Simile website. http://simile.mit.edu/ 41. SPARQL Query Language for RDF. W3C Working Draft 4 October
M. Stonebraker, D. Abadi, A. Batkin, Xuedong Chen, Mitch Cherniack, Miguel Ferreira, Edmond Lau, Amerson Lin, S. Madden, E. O'Neil, P. O'Neil, A. Rasin, Nga Tran, S. Zdonik (2005)
C-Store: A Column-oriented DBMS
Jing Lu, Li Ma, Lei Zhang, Jean-Sébastien Brunner, Chen Wang, Yue Pan, Yong Yu (2007)
SOR: A Practical System for Ontology Storage, Reasoning and Search
Michael Schmidt, Thomas Hornung, Norbert Küchlin, G. Lausen, C. Pinkel (2008)
An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario
Yannis Theoharis, V. Christophides, G. Karvounarakis (2005)
Benchmarking Database Representations of RDF/S Stores
T. Brooks (2010)
World Wide Web Consortium (W3C)
D. Florescu, Donald Kossmann (1999)
Storing and Querying XML Data using an RDMBSIEEE Data Eng. Bull., 22
D. Abadi, S. Madden, Miguel Ferreira (2006)
Integrating compression and execution in column-oriented database systemsProceedings of the 2006 ACM SIGMOD international conference on Management of data
Bill Howe, D. Maier, N. Rayner, J. Rucker (2008)
Quarrying dataspaces: Schemaless profiling of unfamiliar information sources2008 IEEE 24th International Conference on Data Engineering Workshop
K. Wilkinson, C. Sayers, Harumi Kuno, D. Reynolds (2003)
Efficient RDF Storage and Retrieval in Jena2
T. Milo, Dan Suciu (1999)
Index Structures for Path Expressions
A. Kemper, G. Moerkotte (1992)
Access Support Relations: An Indexing Method for Object BasesInf. Syst., 17
J. Broekstra, A. Kampman, F. Harmelen (2002)
Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema
Muralidhar Subramanian, V. Krishnamurthy (1999)
Performance Challenges in Object-Relational DBMSsIEEE Data Eng. Bull., 22
G. Copeland, S. Khoshafian (1985)
A decomposition storage modelProceedings of the 1985 ACM SIGMOD international conference on Management of data
Valerie Bönström, A. Hinze, H. Schweppe (2003)
Storing RDF as a graphProceedings of the IEEE/LEOS 3rd International Conference on Numerical Simulation of Semiconductor Optoelectronic Devices (IEEE Cat. No.03EX726)
J. Shanmugasundaram, K. Tufte, Chun Zhang, G. He, D. DeWitt, J. Naughton (1999)
Relational Databases for Querying XML Documents: Limitations and Opportunities
D. Batory (1978)
On searching transposed filesACM Transactions on Database Systems (TODS), 4
(2006)
Worldwide rdbms 2005 vendor shares
AND B.obj = C.subj AND C.prop = "
D. Abadi, Daniel Myers, D. DeWitt, S. Madden (2007)
Materialization Strategies in a Column-Oriented DBMS2007 IEEE 23rd International Conference on Data Engineering
J. Beckmann, A. Halverson, R. Krishnamurthy, J. Naughton (2006)
Extending RDBMSs To Support Sparse Datasets Using An Interpreted Attribute Storage Format22nd International Conference on Data Engineering (ICDE'06)
Efficient management of RDF data is an important prerequisite for realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance of RDF databases and consider a recent suggestion, “property tables”. We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertically partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds. Encouraged by these results, we describe the architecture of SW-Store, a new DBMS we are actively building that implements these techniques to achieve high performance RDF data management.
The VLDB Journal – Springer Journals
Published: Apr 1, 2009
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