1. 陶承恺 Chengkai Tao tck@live.cn
RDF Stores
S. Sakr and G. A. Naymat.
Relational Processing of RDF Queries: A Survey.
SIGMOD Record, 38:23-28, June 2010.
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2. Classification Based on relational databases Native RDF stores
User
RDF Database
File systems
(In-memory)
Relational database management
systems (RDBMSs) have repeatedly shown that they are
very efficient, scalable and successful in hosting types of data
which have formerly not been anticipated to be stored inside
relational databases
Native: Sesame, Jena TDB
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Classification

3. Relational RDF Stores • Vertical (triple) table stores
• Horizontal (binary) table stores
• Property (n-ary) table stores
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Relational RDF Stores

4. Each RDF triple is stored directly in a three-column table (s, p, o)
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Vertical table stores

5. Advantage: Simple
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Vertical table stores

6. Vertical table stores Disadvantage: Join Example:
retrieve the web page
of the author
of a survey paper
with the title
”Querying RDF Data”
join cost is a major portion of the total processing time
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Vertical table stores

7. Vertical table stores Improvement B-tree Index: CREATE INDEX
Plan generation: RDF-specific statistical synopses
Select T3.Object From Triples as T1, Triples as T2, Triples as T3, Triples as T4 Where T1.Predicate=“publicationType” and T1.Object=“Survey Paper” and T2.predicate=“hasTitle” and T2.Object=“Querying RDF Data” and T3.Predicate=“webPage” and T1.subject=T2.subject and T4.subject=T1.subject and T4.Predicate=“authoredBy” and T4.Object = T3.Subject
Materialized index: storage increase
Exhaustive or as needed
a standard problem in database systems
selectivity estimation has a huge impact on plan generation
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Vertical table stores

8. Horizontal table stores
RDF triples are modeled as one horizontal table or into a set of vertically partitioned binary tables (one table for each RDF property)
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Horizontal table stores

9. Horizontal table stores
Advantage
Straightforward table creation
Efficient when predicate is specified
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Horizontal table stores

10. Horizontal table stores
Disadvantage: dependent
Number of properties
Query type
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Horizontal table stores

11. Horizontal table stores
Improvement
Column-store: Improved bandwidth utilization and data compression
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Horizontal table stores

12. Horizontal table stores
Lefteris Sidirourgos , Romulo Goncalves , Martin Kersten , Niels Nes , Stefan Manegold, Column-store support for RDF data management: not all swans are white, Proceedings of the VLDB Endowment, v.1 n.2, August 2008 This paper reports on the results of an independent evalu- ation of the techniques presented in the VLDB 2007 paper “Scalable Semantic Web Data Management Using Vertical Partitioning”, authored by D. Abadi, A. Marcus, S. R. Mad- den, and K. Hollenbach [1].
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Horizontal table stores

13. multiple RDF properties are modeled as n-ary table columns for the same subject
applications typically have access patterns in which certain subjects and/or properties are accessed together
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Property table stores

14. Property table stores Advantage Similar to relational database
Best performance
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Property table stores

15. Property table stores Disadvantage Complex algorithm
Highly dependent on algorithm, data…
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Property table stores

16. Performance Comparison
A comparison between the alternative relational RDF storage techniques in terms of their query performance (in milliseconds)
TS=Triple Store, BS=Binary Store, RS=Relational Store, PS=Property Store
Dataset=DBLP, Queries=SP2Bench, RDB=DB2
Hooran MahmoudiNasab , Sherif Sakr, An experimental evaluation of relational RDF storage and querying techniques, Proceedings of the 15th international conference on Database systems for advanced applications, April 01-04, 2010, Tsukuba, Japan
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Performance Comparison

17. Property table stores How to create the tables? Specified by user
Subject-Property Matrix Materialized Join Views
an auxiliary structure exploited by an RDBMS optimizer
n-way join = m-properties + (n – m) joins.
Eugene Inseok Chong , Souripriya Das , George Eadon , Jagannathan Srinivasan, An efficient SQL-based RDF querying scheme, Proceedings of the 31st international conference on Very large data bases, August 30-September 02, 2005, Trondheim, Norway
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Property table stores

18. Property table stores How to create the tables? Data mining
RDF graph or query log
Subject-property co-occurrence pattern discovery or query pattern discovery
Applied to schema design or caching strategies to improve performance
L. Ding, K. Wilkinson, C. Sayers, H. Kuno, Application-Specific Schema Design for Storing Large RDF Datasets, Proc. of the 1st International Workshop on Practical and Scalable Semantic Systems, pp , 2003.
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Property table stores

19. Property table stores Apriori 7 subjects, 4 properties
{1,2,3,4}, {1,2}, {2,3,4}, {2,3}, {1,2,4}, {3,4}, and {2,4}
min support=3
Property
Support 1 3 2 6 4 5
Property
Support
{1,2} 3
{1,3} 1
{1,4} 2
{2,3} 6
{2,4} 4
{3,4} 5
Property
Support
{1,2,3} 1
{1,2,4} 2
{1,3,4}
{2,3,4}
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Property table stores

20. Property table stores How to create the tables? Vertical Partitioning
A set of properties P = {P1,P2, ...,Pn} to a set of fragments F = {F1, F2, ..., Fx}
Exploit the affinity matrix
Cluster using Bond Energy Algorithm
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Property table stores

21. Property table stores How to create the tables? Vertical Partitioning
select the top K attributes with the highest total access number
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Property table stores

22. Property table stores How to create the tables? Vertical Partitioning
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Property table stores

23. Property table stores How to create the tables? Vertical Partitioning
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Property table stores

24. Property table stores How to create the tables? Vertical Partitioning
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Property table stores

25. Property table stores How to create the tables? Vertical Partitioning
Partitioning: Z = (QU ∗ QL) − QI2
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Property table stores

26. Property table stores How to create the tables?
Exploit RDF Schema data …
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Property table stores

27. Thanks!
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