Michael L. Brodie, Mark Greaves, Rudi Studer                                                                              ...
Michael L. Brodie, Mark Greaves, Rudi Studer                                                                              ...
www.sti2.org   7/2011 -Summit
Problems - Solutions •  Problem PULL •  Solution PUSHwww.sti2.org            7/2011 -Summit
Tipping PointsTechnology        Time                   Value                Tipping PointDatabases         ‘80s (RDBMSs)  ...
Semantic Web Value & Tipping Point •  Deliver value •  Dirty details of a specific application (domain) •  Dieter’s Exampl...
Big Data •  Problem: Scale •  Solutions               –  Data-driven methods (supplant scientific method?)               –...
Thank you for being here!www.sti2.org                               7/2011 -Summit
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STI Summit 2011 - Conclusion

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STI Summit 2011 - Conclusion

  1. 1. Michael L. Brodie, Mark Greaves, Rudi Studer STI Fellows© Copyright 2011 STI - INTERNATIONAL www.sti2.org
  2. 2. Michael L. Brodie, Mark Greaves, Rudi Studer STI Fellows© Copyright 2011 STI - INTERNATIONAL www.sti2.org
  3. 3. www.sti2.org 7/2011 -Summit
  4. 4. Problems - Solutions •  Problem PULL •  Solution PUSHwww.sti2.org 7/2011 -Summit
  5. 5. Tipping PointsTechnology Time Value Tipping PointDatabases ‘80s (RDBMSs) Data Beat other management DBMSsClient-Server ‘90s (servers) Cost, flexibility Beat M/FServices 20th C (economics); Module-object- simplicity 90s (Biz), 00s(IT) serviceWeb 80-90s; 90s+ Universal Mosaic Information access and sharingCloud 80’s; 2004+ Compression: Big players savings: hardware; labor AmazonSemantic Tech 50’s; 70-80’s; 90’s+ Higher order + AI Winter; now productivity what?Semantic Web 2001 (Sci Am); Connectivity: people 10 years; now 2005 (LOD)+ & machines what? www.sti2.org 7/2011 -Summit
  6. 6. Semantic Web Value & Tipping Point •  Deliver value •  Dirty details of a specific application (domain) •  Dieter’s Examples –  Documents –  Data –  Mobile –  Social –  Sensors –  Energy grids –  Etc.www.sti2.org 7/2011 -Summit
  7. 7. Big Data •  Problem: Scale •  Solutions –  Data-driven methods (supplant scientific method?) –  Multi-disciplinary (will the semantic web be in the mix?) •  Data Integration Solution –  Search / discover candidates –  ETL: get candidate data elements in required form –  Entity resolution –  Computation •  Critical / Central Challenge –  Meaningfullywww.sti2.org 7/2011 -Summit
  8. 8. Thank you for being here!www.sti2.org 7/2011 -Summit
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