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Achieving time effective federated information from scalable rdf data using sparql queries

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  • 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 01/20/14 NCACPR-14 1
  • 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. 01/20/14 NCACPR-14 2
  • 3. My LINKED IN Profile 01/20/14 NCACPR-14 3
  • 4. My International Conf. Title 01/20/14 NCACPR-14 4
  • 5. Web 2.0 Architecture 01/20/14 NCACPR-14 5
  • 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. 01/20/14 NCACPR-14 6
  • 7. Web 3.0 Architecture 01/20/14 NCACPR-14 7
  • 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). 01/20/14 NCACPR-14 8
  • 9. RDF Data Graph 01/20/14 NCACPR-14 9
  • 10. RDF Tabulator Screenshot 01/20/14 NCACPR-14 10
  • 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. 01/20/14 NCACPR-14 11
  • 12. SPARQL QUERY IN TWINKLE FEDERATED INFORMATION OF LINKEDIN AUTHOR AND ICHCI INTL. CONF. TITLE OF THE SAME PERSON DATA 01/20/14 NCACPR-14 12
  • 13. SPARQL QUERY PROCESSING • SPARQL queries are executed against RDF datasets, consisting of RDF graphs. • A SPARQL endpoint accepts queries and returns results via HTTP. • SPARQL endpoints will query any Web-accessible RDF data. • The results of SPARQL queries can be returned and/or rendered in a variety of formats: – – – – – 01/20/14 XML JSON RDF HTML CSV NCACPR-14 13
  • 14. ANALYSIS OF RDF DATA GRAPH FOR FEDERATED QUERY I. SCALABLE TRIPLES VISUALIZATION 01/20/14 NCACPR-14 14
  • 15. II. RESPONSE TIME OF SPARQL QUERY ON ‘n’ (VIRTUAL) OBJECTS 01/20/14 NCACPR-14 15
  • 16. SELECTIVITY: • • • • 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. 01/20/14 NCACPR-14 16
  • 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. 01/20/14 NCACPR-14 17