1. Achieving Time Effective Federated
Information from scalable RDF data using
SPARQL Queries
By
D.Teja Santosh, Assistant Professor
Computer Science and Engineering
GITAM University,
Rudraram, Hyderabad
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2. Aim: To retrieve federated information from scalable RDF data using SPARQL
query as a “Global Service” in very less time.
Technologies Used:
RDF, SPARQL,OWL.
Novelty of the concept:
 Integrating heterogeneous data from different databases is shown using
a single RDF data file.
 Retrieving federated data using SPARQL query as a Global Web Service.
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3. My LINKED IN Profile
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4. My International Conf. Title
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5. Web 2.0 Architecture
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6. Issues in integrating two different web services
• Different platforms.
• Different languages/technologies.
• Individual XML data viewed as Tree that may lead to
inconsistent responses when integrated.
• Availability of web services.
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7. Web 3.0 Architecture
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8. How RDF overcomes this issue?
•
The RDF model is made up of triples: subject-predicate-object.
•
These triples are uniquely identified on the web through URI. [Like “PASSPORT
NUMBER” to uniquely identify a person across the real world].
•
This lets machines understand human knowledge statements. [Computer saying:
Oh!]
•
The RDF model is essentially the canonicalization of a (directed) graph, and so as
such has all the advantages (and generality) of structuring information using
graphs
•
Any number of author profile and corresponding conference title data (federation)
in combined format are linked and retrieved due to the query pointing to the
generic subject node(s).
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9. RDF Data Graph
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10. RDF Tabulator Screenshot
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11. SPARQL
•
I call SPARQL as a test bed which makes us to have clear idea about the result
accuracy (as a Web 3.0 learner).
•
Queries RDF data. If your data is in RDF, then SPARQL can query it natively.
•
Implicit join syntax. SPARQL queries RDF graphs, which consist of various triples
expressing binary relations between resources. As all relationships are of a fixed
size and data lives in a single graph, SPARQL does not require explicit joins that
specify the relationship between differently structured data.
•
The SPARQL query above has a similar structure:
SELECT <variable list>
WHERE {<graph pattern> }
•
FROM is used as a Base URL of the RDF Triple Store.
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12. SPARQL QUERY IN TWINKLE
FEDERATED INFORMATION OF LINKEDIN AUTHOR AND ICHCI INTL.
CONF. TITLE OF THE SAME PERSON DATA
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13. SPARQL QUERY PROCESSING
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SPARQL queries are executed against RDF datasets, consisting of RDF graphs.
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A SPARQL endpoint accepts queries and returns results via HTTP.
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SPARQL endpoints will query any Web-accessible RDF data.
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The results of SPARQL queries can be returned and/or rendered in a variety of formats:
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XML
JSON
RDF
HTML
CSV
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14. ANALYSIS OF RDF DATA GRAPH FOR FEDERATED QUERY
I. SCALABLE TRIPLES VISUALIZATION
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15. II. RESPONSE TIME OF SPARQL QUERY ON ‘n’ (VIRTUAL) OBJECTS
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16. SELECTIVITY:
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sel(t) = sel(s) * sel(p) * sel(o)
sel(s) = 1/R, R - No. of Resources.
sel(p) = Tp/T, T – Total No. of triples, Tp – Triples matching predicate p.
sel(o) = hc(p,oc)/Tp, where (p,oc) represents the class of the histogram for predicate p in which
object o falls.
INFERENCE:
When Selectivity parameter is used for analysis of SPARQL query, accessing the data using subject
is encouraged when federated response is required.
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17. REFERENCES
[1] RDF and SOA by David Booth, Ph.D., HP Software.
[2] TechnicaLeeSpeaking: Software designs, implementations, solutions, and
musings by Lee Feigenbaum.
[3] Frank Manola, Eric Miller, W3C, RDF Primer.
[4] David Booth, W3C Fellow / Hewlett-Packard, Hugo Haas, W3C Francis
McCabe, Fujitsu Labs of America, Eric Newcomer (until October 2003),
Iona Michael Champion (until March 2003), Software AG, Chris Ferris
(until March 2003), IBM, David Orchard (until March 2003), BEA Systems,
Web Services Architecture.
[5] Eric Prud'hommeaux, W3C ,Andy Seaborne, Hewlett-Packard
Laboratories, Bristol, SPARQL Query Language for RDF,W3C
Recommendation 15 January 2008.
[6] Lesley Charles, November 23, 2009, SPARQL Query Optimization.
[7] A. Bernstein, M. Stocker, and C. Kiefer. SPARQL Query Optimization Using
Selectivity Estimation. InPoster Proceedings of the 6th International
Semantic Web Conference (ISWC), Lecture Notes in Computer
Science.Springer, 2007.
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