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Database Research Principles Revealed

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Research Principles Revealed ,[object Object],[object Object]
But First, Some Thanks ,[object Object],[object Object],[object Object]
Extra-Special #1 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extra-Special #2 ,[object Object],[object Object],[object Object],Details Intuition
Extra-Special #3 and #4 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Superb Ph.D. Students
Terrific Collaborators* ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Shel Finkelstein Alon Halevy Rajeev Motwani Anand Rajaraman Shuky Sagiv Janet Wiener * Significant # co-authored papers in DBLP
[object Object]
Research Principles Revealed ,[object Object],[object Object],[object Object],Disclaimer These principles work for me. Your mileage may vary!
Major Research Areas Active Databases Data Warehousing Semistructured Data “ Lore” Data Streams Uncertainty and Lineage “ Trio” Constraints Triggers Incremental View Maintenance Incremental View Maintenance Triggers Incremental View Maintenance Lineage
Major Research Areas Active Databases Data Warehousing Semistructured Data “ Lore” Data Streams Uncertainty and Lineage “ Trio” Incremental View Maintenance Constraints Triggers Lineage Incremental View Maintenance
Finding Research Areas ,[object Object],[object Object],[object Object],[object Object]
Finding Research Areas Active Databases Data Warehousing Semistructured Data Data Streams Uncertainty and Lineage Data Integration
Finding Research Areas Uncertainty and Lineage
Finding Research Topics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Finding Research Topics Semistructured data Data streams Uncertain data
The Research Itself ,[object Object],[object Object],[object Object],[object Object],[object Object],Data Model Query Language System
[object Object],Nailing Down a New Data Model
[object Object],[object Object],[object Object],[object Object],[object Object],Nailing Down a New Data Model
Nailing Down a New Data Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Nailing Down a New Data Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Nailing Down a New Data Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Model for Trio Project Relative expressiveness Closure properties Only “complete” model Only understandable models In the end, lineage saved the day Possible models R
The Research Triple Data Model Query Language System Query Language
Query Language Design ,[object Object],[object Object],[object Object],[object Object],[object Object],Data Model Query Language System
The IBM-Almaden Years ,[object Object],“ We finished our rule system ages ago” Transition tables, Conflicts, Confluence, … “ Write Code!”
The IBM-Almaden Years ,[object Object],“ We finished our rule system ages ago” “ Yeah, but what does it do?”
The IBM-Almaden Years ,[object Object],“ Yeah, but what does it do?” “ Umm … I’ll need to run it to find out”
The IBM-Almaden Years ,[object Object],“ Umm … I’ll need to run it to find out” Disclaimer These principles work for me. Your mileage may vary.
Tricky Semantics Example #1 ,[object Object],[object Object],<Student> <ID> 123 </ID> <Name> Susan </Name> <Major> CS </Major> </Student> <Student> ● ● ● </Student> ,[object Object],[object Object],[object Object]
Tricky Semantics Example #1 ,[object Object],[object Object],<Student> <ID> 123 </ID> <Name> Susan </Name> <Major> CS </Major> </Student> <Student> ● ● ● </Student> ,[object Object],[object Object],[object Object]
Tricky Semantics Example #1 ,[object Object],[object Object],<Student> <ID> 123 </ID> <Advisor> Garcia </Advisor> <Advisor> Widom </Advisor> </Student> <Student> ● ● ● </Student> ,[object Object],[object Object]
Tricky Semantics Example #2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Tricky Semantics Example #3 ,[object Object],[object Object],[object Object],[object Object]
Tricky Semantics Example #3 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Tricky Semantics Example #3 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],The “It’s Just SQL” Trap SELECT S.temp FROM Sigmod S, Climate C WHERE S.loc = C.loc AND S.year = 2010 London ∥ New York 2010 Sigmod (year, loc ) [ 64 – 79 ] New York [ 55 – 68 ] London Climate (loc, temp )
The “It’s Just SQL” Trap ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Taming the Semantic Trickiness ,[object Object],[object Object],[object Object],Result D D 1 , D 2 , …, D n possible instances Q on each instance representation of instances Q(D 1 ), Q(D 2 ), …, Q ( D n ) (implementation) 30 years of refinement
Taming the Semantic Trickiness ,[object Object],[object Object],[object Object],Streams Relations Window Istream / Dstream 30 years of refinement Relational
Taming the Semantic Trickiness ,[object Object],[object Object],[object Object],[object Object],3 years of refinement
The Research Triple System Data Model Query Language System Impact “ Write Code!”
Truth in Advertising System Data Model Query Language System ,[object Object],[object Object]
Disseminating Research Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary: Five Points ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Intuition Details
Summary: Five Points ,[object Object],[object Object],[object Object],Incremental View Maintenance
Thank You Serge Abiteboul Brian Babcock Elena Baralis Omar Benjelloun Sudarshan Chawathe Bobbie Cochrane Shel Finkelstein Alon Halevy Rajeev Motwani Anand Rajaraman Shuky Sagiv Janet Wiener

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Database Research Principles Revealed

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 7.
  • 8.
  • 9.
  • 10. Major Research Areas Active Databases Data Warehousing Semistructured Data “ Lore” Data Streams Uncertainty and Lineage “ Trio” Constraints Triggers Incremental View Maintenance Incremental View Maintenance Triggers Incremental View Maintenance Lineage
  • 11. Major Research Areas Active Databases Data Warehousing Semistructured Data “ Lore” Data Streams Uncertainty and Lineage “ Trio” Incremental View Maintenance Constraints Triggers Lineage Incremental View Maintenance
  • 12.
  • 13. Finding Research Areas Active Databases Data Warehousing Semistructured Data Data Streams Uncertainty and Lineage Data Integration
  • 14. Finding Research Areas Uncertainty and Lineage
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23. Data Model for Trio Project Relative expressiveness Closure properties Only “complete” model Only understandable models In the end, lineage saved the day Possible models R
  • 24. The Research Triple Data Model Query Language System Query Language
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42. The Research Triple System Data Model Query Language System Impact “ Write Code!”
  • 43.
  • 44.
  • 45.
  • 46.
  • 47. Thank You Serge Abiteboul Brian Babcock Elena Baralis Omar Benjelloun Sudarshan Chawathe Bobbie Cochrane Shel Finkelstein Alon Halevy Rajeev Motwani Anand Rajaraman Shuky Sagiv Janet Wiener