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SPARQL for Business Intelligence Orri Erling - Program Manager, Virtuoso © 2008 OpenLink Software, All rights reserved.
For The Data Web to Deliver Promise of limitless integration and analysis Existing SQL workloads should naturally fall within SPARQL’s scope Surfing and joining between relational data and RDF should be seamless © 2008 OpenLink Software, All rights reserved.
What Is Missing? Aggregation Nested Queries Expressions © 2008 OpenLink Software, All rights reserved.
Mapped vs. Physical RDF Predicates are unspecified Combining lots off different data sources Lots of A-box inference (SameAs, Transitivity) Mapped is better, if: © 2008 OpenLink Software, All rights reserved.  Can push all to RDBMS - about 10x faster Data is time-sensitive, frequently changing, very large Physical is better, if:
When defining mappings... Be careful when many tables make one entity - you may get lots of unions Make explicit IRI scheme to limit pointless joining Know what the mapper and SQL can and cannot optimize © 2008 OpenLink Software, All rights reserved.
Some Present Work Refining generated SQL There is no reason why SPARQL mapped should not equal SQL in performance, when going to single DBMS © 2008 OpenLink Software, All rights reserved.
Joining Between Mapped and Physical Special SQL logic is needed when joining IRI IDs of physical quads with IRI strings of virtual triples SameAs requires special attention © 2008 OpenLink Software, All rights reserved.
Use Cases OpenLink MIS All accounts, CRM, products, emails, support cases have URIs MusicBrainz OpenLink Data Spaces PHPbb, Mediawiki, Drupal, etc. © 2008 OpenLink Software, All rights reserved.
OpenLink Software © 2008 OpenLink Software, All rights reserved.  Thank You! http://virtuoso.openlinksw.com

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ESWC2008 SPARQL BI OpenLink- SPARQL for Business Intelligence

  • 1. SPARQL for Business Intelligence Orri Erling - Program Manager, Virtuoso © 2008 OpenLink Software, All rights reserved.
  • 2. For The Data Web to Deliver Promise of limitless integration and analysis Existing SQL workloads should naturally fall within SPARQL’s scope Surfing and joining between relational data and RDF should be seamless © 2008 OpenLink Software, All rights reserved.
  • 3. What Is Missing? Aggregation Nested Queries Expressions © 2008 OpenLink Software, All rights reserved.
  • 4. Mapped vs. Physical RDF Predicates are unspecified Combining lots off different data sources Lots of A-box inference (SameAs, Transitivity) Mapped is better, if: © 2008 OpenLink Software, All rights reserved. Can push all to RDBMS - about 10x faster Data is time-sensitive, frequently changing, very large Physical is better, if:
  • 5. When defining mappings... Be careful when many tables make one entity - you may get lots of unions Make explicit IRI scheme to limit pointless joining Know what the mapper and SQL can and cannot optimize © 2008 OpenLink Software, All rights reserved.
  • 6. Some Present Work Refining generated SQL There is no reason why SPARQL mapped should not equal SQL in performance, when going to single DBMS © 2008 OpenLink Software, All rights reserved.
  • 7. Joining Between Mapped and Physical Special SQL logic is needed when joining IRI IDs of physical quads with IRI strings of virtual triples SameAs requires special attention © 2008 OpenLink Software, All rights reserved.
  • 8. Use Cases OpenLink MIS All accounts, CRM, products, emails, support cases have URIs MusicBrainz OpenLink Data Spaces PHPbb, Mediawiki, Drupal, etc. © 2008 OpenLink Software, All rights reserved.
  • 9. OpenLink Software © 2008 OpenLink Software, All rights reserved. Thank You! http://virtuoso.openlinksw.com