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[object Object],[object Object],[object Object]
The WWW is Great
 
False Information on the Web (I) Maurice Jarre (1924-2009)  French Conductor and Composer “ One could say my life itself has been one long soundtrack. Music was my life, music brought me to life, and music is how I will be remembered long after I leave this life. When I die there will be a final waltz playing in my head and that only I can hear.” 2:29, 30 March 2009
False Information on the Web  (II) Posted by Andrew Breitbart In his blog …
[object Object],The Internet needs a way to help people separate rumor from real science. –  Tim Berners-Lee
Why is the Problem Hard? (A Well-Predicted Problem) ,[object Object],[object Object],S1 S2 S3 Stonebraker MIT Berkeley MIT Dewitt MSR MSR UWisc Bernstein MSR MSR MSR Carey UCI AT&T BEA Halevy Google Google UW
Why is the Problem Hard? (A Well-Predicted Problem) ,[object Object],[object Object],[object Object],S1 S2 S3 Stonebraker MIT Berkeley MIT Dewitt MSR MSR UWisc Bernstein MSR MSR MSR Carey UCI AT&T BEA Halevy Google Google UW
Why is the Problem Hard? (A Well-Predicted Problem) ,[object Object],[object Object],S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW
Our Goal: Truth Discovery w. Awareness of Dependence Between Sources ,[object Object],[object Object],S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW
Challenges in Dependence Discovery ,[object Object],[object Object],3. A copier can also provide or verify some data by itself, so it is inappropriate to ignore all of its data. S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW
High-Level Intuitions for Dependence Detection ,[object Object],[object Object],[object Object]
Dependence? ,[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],[object Object],[object Object],[object Object],Are Source 1 and Source 2 dependent? Not necessarily        
Dependence?  ,[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],[object Object],[object Object],[object Object],Are Source 1 and Source 2 dependent? -- Common Errors Very likely       
High-Level Intuitions for Dependence Detection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Dependence?  ,[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],[object Object],[object Object],[object Object],Are Source 1 and Source 2 dependent? -- Different Accuracy    S1 more likely to be a copier        
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Source Dependence ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Models for a Static World ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Depen AccuPR Consider value probabilities in dependence analysis  Accu Remove Cond 1 Sim Remove Cond 3 NonUni Remove Cond 2
Bayesian Analysis – Basic ,[object Object],[object Object],[object Object],[object Object],[object Object],Different  Values  O.A d TRUE  O.A t S1    S2 FALSE  O.A f Same Values
Bayesian Analysis –  Probability Computation ,[object Object],  > Pr Independence Copying O.A t O.A f O.A d Different  Values  O.A d TRUE  O.A t S1    S2 FALSE  O.A f Same Values
Considering Source Accuracy ≠ ≠ Pr Independence S1 Copies S2 S2 Copies S1 O.A t O.A f O.A d Different  Values  O.A d TRUE  O.A t S1    S2 FALSE  O.A f Same Values
II. Finding the True Value ,[object Object]
Solution on the Motivating Example Copying Relationship UCI AT&T BEA Truth Discovery (1-.99*.8=.2) (.2 2 ) S1 S2 S3 S4 S5 Round 1 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .87 .2 .2 .99 .99 .99
Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 S3 S4 S5 UCI S1 Round 2 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .14 .49 .49 .49 .08 .49 .49 .49
Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 S3 S4 S5 UCI S1 Round 3 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .12 .49 .49 .49 .06 .49 .49 .49
Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 UCI S1 Round 4 S3 S4 S5 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .10 .48 .49 .50 .05 .49 .48 .50
Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 UCI S1 Round 5 S3 S4 S5 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .09 .47 .49 .51 .04 .49 .47 .51
Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 UCI S1 Round 13 S3 S4 S5 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .55 .49 .55 .49 .44 .44
Combining Accuracy and Dependence ,[object Object],[object Object],Truth  Discovery Source-accuracy Computation Dependence Detection Step 1 Step 3 Step 2
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Setup ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Naïve Voting and Types of Errors ,[object Object]
Contributions of Various Components Precision improves by 25.4% over Naïve Considering dependence improves the results most Reasonably fast Methods Prec #Rnds Time(s) Naïve .71 1 .2 Only value similarity .74 1 .2 Only source accuracy .79 23 1.1 Only source dependence .83 3 28.3 Depen+accu .87 22 185.8 Depen+accu+sim .89 18 197.5
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Integration Faces 3 Challenges
Data Integration Faces 3 Challenges
Data Integration Faces 3 Challenges Scissors Paper Scissors
Data Integration Faces 3 Challenges Scissors Glue
Data Integration Faces 3 Challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Assume INDEPENDENCE of data sources
Source Copying Adds  A New Dimension to Data Integration
Copying Can Be Large Scaled  [VLDB’10a] (Copying of AbeBooks Data)  Data collected from AbeBooks [Yin et al., 2007]
Related  Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]

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Integrating Conflicting Data_PVERConf_May2011

  • 1.
  • 2. The WWW is Great
  • 3.  
  • 4. False Information on the Web (I) Maurice Jarre (1924-2009) French Conductor and Composer “ One could say my life itself has been one long soundtrack. Music was my life, music brought me to life, and music is how I will be remembered long after I leave this life. When I die there will be a final waltz playing in my head and that only I can hear.” 2:29, 30 March 2009
  • 5. False Information on the Web (II) Posted by Andrew Breitbart In his blog …
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23. Considering Source Accuracy ≠ ≠ Pr Independence S1 Copies S2 S2 Copies S1 O.A t O.A f O.A d Different Values O.A d TRUE O.A t S1  S2 FALSE O.A f Same Values
  • 24.
  • 25. Solution on the Motivating Example Copying Relationship UCI AT&T BEA Truth Discovery (1-.99*.8=.2) (.2 2 ) S1 S2 S3 S4 S5 Round 1 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .87 .2 .2 .99 .99 .99
  • 26. Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 S3 S4 S5 UCI S1 Round 2 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .14 .49 .49 .49 .08 .49 .49 .49
  • 27. Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 S3 S4 S5 UCI S1 Round 3 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .12 .49 .49 .49 .06 .49 .49 .49
  • 28. Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 UCI S1 Round 4 S3 S4 S5 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .10 .48 .49 .50 .05 .49 .48 .50
  • 29. Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 UCI S1 Round 5 S3 S4 S5 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .09 .47 .49 .51 .04 .49 .47 .51
  • 30. Solution on the Motivating Example Copying Relationship AT&T BEA Truth Discovery S2 UCI S1 Round 13 S3 S4 S5 S1 S2 S3 S4 S5 Stonebraker MIT Berkeley MIT MIT MS Dewitt MSR MSR UWisc UWisc UWisc Bernstein MSR MSR MSR MSR MSR Carey UCI AT&T BEA BEA BEA Halevy Google Google UW UW UW S 1 S 2 S 4 S 3 S 5 .55 .49 .55 .49 .44 .44
  • 31.
  • 32.
  • 33.
  • 34.
  • 35. Contributions of Various Components Precision improves by 25.4% over Naïve Considering dependence improves the results most Reasonably fast Methods Prec #Rnds Time(s) Naïve .71 1 .2 Only value similarity .74 1 .2 Only source accuracy .79 23 1.1 Only source dependence .83 3 28.3 Depen+accu .87 22 185.8 Depen+accu+sim .89 18 197.5
  • 36.
  • 37. Data Integration Faces 3 Challenges
  • 38. Data Integration Faces 3 Challenges
  • 39. Data Integration Faces 3 Challenges Scissors Paper Scissors
  • 40. Data Integration Faces 3 Challenges Scissors Glue
  • 41.
  • 42. Source Copying Adds A New Dimension to Data Integration
  • 43. Copying Can Be Large Scaled [VLDB’10a] (Copying of AbeBooks Data) Data collected from AbeBooks [Yin et al., 2007]
  • 44.
  • 45.

Editor's Notes

  1. While I expected online blogs and maybe some smaller papers to use the quote, I did not think it would have a major impact. I was wrong. Quality newspapers in England, India, America and as far away as Australia had my words in their reports of Jarre’s death. I was shocked that highly respected newspapers would use material from Wikipedia without first sourcing and referencing it properly.
  2. On July 19, 2010, Shirley Sherrod was forced to resign from her position as Georgia State Director of Rural Development for the United States Department of Agriculture [1] after conservative blogger Andrew Breitbart posted video excerpts of Sherrod's address at a March 2010 NAACP event to his website. [2] The NAACP condemned her remarks, and U.S. government officials called on her to resign. However, upon review of the unedited video in context, the NAACP, White House officials, and Tom Vilsack , the Secretary of Agriculture , apologized and Sherrod was offered a new position. The first news outlet to report on the Breitbart video was FoxNews.com , which posted an article about the story on its website. [16] [14] The New York City affiliate for CBS also posted a report on its website later that afternoon. [14] The Atlanta Journal Constitution newspaper's related website also soon picked up the story. [17] In addition, the story was picked up and reported widely in the blogosph A ccording to Sherrod, that afternoon she received numerous demands from government officials to submit her resignation, demands that she characterized as harrassment. [18] In response to a call from USDA deputy undersecretary Cheryl Cook, Sherrod submitted her resignation via email. Sherrod claims that Cook told her White House officials wanted her to quit immediately because the controversy was "going to be on Glenn Beck tonight [18] ", a claim disputed by White House Press Secretary Robert Gibbs. [6] ere . [14]
  3. ( American short-story Writer and Novelist , Nobel Prize for Literature in 1949, 1897 - 1962 )
  4. ( American short-story Writer and Novelist , Nobel Prize for Literature in 1949, 1897 - 1962 )
  5. ( American short-story Writer and Novelist , Nobel Prize for Literature in 1949, 1897 - 1962 )
  6. ( American short-story Writer and Novelist , Nobel Prize for Literature in 1949, 1897 - 1962 )
  7. ( American short-story Writer and Novelist , Nobel Prize for Literature in 1949, 1897 - 1962 )
  8. ( American short-story Writer and Novelist , Nobel Prize for Literature in 1949, 1897 - 1962 )
  9. ( American short-story Writer and Novelist , Nobel Prize for Literature in 1949, 1897 - 1962 )
  10. ( American short-story Writer and Novelist , Nobel Prize for Literature in 1949, 1897 - 1962 )
  11. ( American short-story Writer and Novelist , Nobel Prize for Literature in 1949, 1897 - 1962 )