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Trio:  A System for  Data ,  Uncertainty , and  Lineage Search “stanford trio” DATA UNCERTAINTY LINEAGE
Stanford Report: March ‘06
Trio <ul><li>Data </li></ul><ul><ul><li>Student #123 is majoring in Econ:   (123,Econ)     Major </li></ul></ul><ul><li>U...
The Picture Data Uncertainty Lineage (“sourcing”)
Why Uncertainty + Lineage? <ul><li>Many applications seem to need both </li></ul><ul><ul><li>Information extraction system...
Why Uncertainty + Lineage? <ul><li>From a technical standpoint, it turns out that  </li></ul><ul><li>lineage... </li></ul>...
Goal <ul><li>A new kind of DBMS in which: </li></ul><ul><ul><li>Data </li></ul></ul><ul><ul><li>Uncertainty </li></ul></ul...
The Trio Trio <ul><li>Data Model </li></ul><ul><ul><li>Simplest extension to relational model that’s sufficiently expressi...
The Trio Trio <ul><li>Data Model </li></ul><ul><ul><li>Uncertainty-Lineage Databases (ULDBs)   </li></ul></ul><ul><li>Quer...
Running Example: Crime-Solving <ul><li>Saw( witness , car )  // may be uncertain </li></ul><ul><li>Drives( person , car ) ...
Data Model: Uncertainty <ul><li>An  uncertain database  represents a set of </li></ul><ul><li>possible instances </li></ul...
Our Model for Uncertainty <ul><li>1. Alternatives </li></ul><ul><li>2. ‘?’ (Maybe) Annotations </li></ul><ul><li>3. Confid...
Our Model for Uncertainty <ul><li>1. Alternatives:   uncertainty about value </li></ul><ul><li>2. ‘?’ (Maybe) Annotations ...
Our Model for Uncertainty <ul><li>1. Alternatives </li></ul><ul><li>2.   ‘?’ (Maybe):  uncertainty about presence </li></u...
Our Model for Uncertainty <ul><li>1. Alternatives </li></ul><ul><li>2. ‘?’ (Maybe) Annotations </li></ul><ul><li>3. Confid...
Deficiency in Model Suspects   =  π person (Saw  ⋈  Drives) ? ? ? Does not correctly capture possible instances in the res...
Lineage to the Rescue <ul><li>Lineage (provenance): “where data came from” </li></ul><ul><ul><li>Internal lineage </li></u...
Example with Lineage ? ? ? Suspects   =  π person (Saw  ⋈  Drives) λ (31) = (11,2),(21,2) λ (32,1) = (11,1),(22,1);  λ (32...
Uncertainty-Lineage Databases ( ULDBs ) <ul><li>1. Alternatives </li></ul><ul><li>2. ‘?’ (Maybe) Annotations </li></ul><ul...
Querying ULDBs <ul><li>Simple extension to SQL </li></ul><ul><li>Formal semantics, intuitive meaning </li></ul><ul><li>Que...
Initial TriQL Example SELECT Drives.person INTO Suspects FROM Saw, Drives WHERE Saw.car = Drives.car ? ? ? λ (31) = (11,2)...
Formal Semantics <ul><li>Query  Q   on ULDB  D </li></ul>D D 1 ,  D 2 , …,  D n possible instances Q   on each instance re...
TriQL: Querying Confidences <ul><li>Built-in function:   Conf() </li></ul><ul><li>SELECT Drives.person INTO Suspects </li>...
TriQL: Querying Lineage <ul><li>Built-in join predicate:   Lineage() </li></ul><ul><li>SELECT Saw.witness INTO AccusesHank...
Operational Semantics <ul><li>Over conventional relational database: </li></ul><ul><li>For each tuple in cross-product of ...
Operational Semantics <ul><li>Over ULDB: </li></ul><ul><li>For each tuple in cross-product of  X 1 , X 2 , ..., X n </li><...
Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy, Honda)  ∥  (Cathy,...
Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy, Honda)  ∥  (Cathy,...
Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy, Honda)  ∥  (Cathy,...
Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy, Honda)  ∥  (Cathy,...
Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy,Honda,Jim,Mazda) ∥ ...
Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy,Honda,Jim,Mazda) ∥ ...
Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy,Honda,Jim,Mazda) ∥ ...
Operational Semantics: Example SELECT Drives.person   INTO Suspects FROM Saw, Drives WHERE Saw.car = Drives.car ? ? λ ( ) ...
Confidences <ul><li>Confidences supplied with base data </li></ul><ul><li>Trio computes confidences on query results </li>...
Additional Query Constructs <ul><li>“ Horizontal subqueries” </li></ul><ul><ul><li>Refer to tuple alternatives as a relati...
Final Example Query Credibility List suspects with  conf  values based on accuser credibility (1, Amy, Jimmy)  ∥  (1, Bett...
Final Example Query Credibility SELECT suspect, score/[sum(score)] as conf FROM (SELECT suspect, (SELECT score FROM Credib...
Final Example Query Credibility SELECT suspect, score/[sum(score)]  as conf FROM (SELECT suspect, (SELECT score FROM Credi...
Final Example Query Credibility SELECT suspect,  score/[sum(score)]   as conf FROM (SELECT suspect, (SELECT score FROM Cre...
Trio System: Version 1 Standard relational DBMS Trio API and translator (Python) Command-line client Trio Metadata TrioExp...
Future Features (sample) <ul><li>Uncertainty </li></ul><ul><ul><li>Incomplete relations </li></ul></ul><ul><ul><li>Continu...
but don’t forget the lineage… DATA UNCERTAINTY LINEAGE
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  1. 1. Trio: A System for Data , Uncertainty , and Lineage Search “stanford trio” DATA UNCERTAINTY LINEAGE
  2. 2. Stanford Report: March ‘06
  3. 3. Trio <ul><li>Data </li></ul><ul><ul><li>Student #123 is majoring in Econ: (123,Econ)  Major </li></ul></ul><ul><li>Uncertainty </li></ul><ul><ul><li>Student #123 is majoring in Econ or CS: </li></ul></ul><ul><ul><ul><li>(123, Econ ∥ CS)  Major </li></ul></ul></ul><ul><ul><li>With confidence 60% student #456 is a CS major: </li></ul></ul><ul><ul><ul><li>(456, CS: 0.6)  Major </li></ul></ul></ul><ul><li>Lineage </li></ul><ul><ul><li>(456)  HardWorker derived from: </li></ul></ul><ul><ul><ul><li>(456, CS)  Major </li></ul></ul></ul><ul><ul><ul><li>“ CS is hard”  some web page </li></ul></ul></ul>
  4. 4. The Picture Data Uncertainty Lineage (“sourcing”)
  5. 5. Why Uncertainty + Lineage? <ul><li>Many applications seem to need both </li></ul><ul><ul><li>Information extraction systems </li></ul></ul><ul><ul><li>Scientific and sensor data management </li></ul></ul><ul><ul><li>Information integration </li></ul></ul><ul><ul><li>Deduplication (data cleaning) </li></ul></ul><ul><ul><li>Approximate query processing </li></ul></ul>
  6. 6. Why Uncertainty + Lineage? <ul><li>From a technical standpoint, it turns out that </li></ul><ul><li>lineage... </li></ul><ul><ul><li>Enables simple and consistent representation of uncertain data </li></ul></ul><ul><ul><li>Correlates uncertainty in query results with uncertainty in the input data </li></ul></ul><ul><ul><li>Can make computation over uncertain data more efficient </li></ul></ul>
  7. 7. Goal <ul><li>A new kind of DBMS in which: </li></ul><ul><ul><li>Data </li></ul></ul><ul><ul><li>Uncertainty </li></ul></ul><ul><ul><li>Lineage </li></ul></ul><ul><li>are all first-class interrelated concepts </li></ul><ul><li>With all the usual DBMS features </li></ul><ul><ul><li>Scalable, reliable, efficient, ad-hoc declarative </li></ul></ul><ul><ul><li>queries and updates, … </li></ul></ul>Trio }
  8. 8. The Trio Trio <ul><li>Data Model </li></ul><ul><ul><li>Simplest extension to relational model that’s sufficiently expressive </li></ul></ul><ul><li>Query Language </li></ul><ul><ul><li>Simple extension to SQL with well-defined semantics and intuitive behavior </li></ul></ul><ul><li>System </li></ul><ul><ul><li>A complete open-source DBMS that people want to use </li></ul></ul>
  9. 9. The Trio Trio <ul><li>Data Model </li></ul><ul><ul><li>Uncertainty-Lineage Databases (ULDBs) </li></ul></ul><ul><li>Query Language </li></ul><ul><ul><li>TriQL </li></ul></ul><ul><li>System </li></ul><ul><ul><li>First prototype built on top of standard DBMS </li></ul></ul>
  10. 10. Running Example: Crime-Solving <ul><li>Saw( witness , car ) // may be uncertain </li></ul><ul><li>Drives( person , car ) // may be uncertain </li></ul><ul><li>Suspects( person ) = π person (Saw ⋈ Drives) </li></ul>
  11. 11. Data Model: Uncertainty <ul><li>An uncertain database represents a set of </li></ul><ul><li>possible instances </li></ul><ul><ul><li>Amy saw either a Honda or a Toyota </li></ul></ul><ul><ul><li>Jimmy drives a Toyota, a Mazda, or both </li></ul></ul><ul><ul><li>Betty saw an Acura with confidence 0.5 or a Toyota with confidence 0.3 </li></ul></ul><ul><ul><li>Hank is a suspect with confidence 0.7 </li></ul></ul>
  12. 12. Our Model for Uncertainty <ul><li>1. Alternatives </li></ul><ul><li>2. ‘?’ (Maybe) Annotations </li></ul><ul><li>3. Confidences </li></ul>
  13. 13. Our Model for Uncertainty <ul><li>1. Alternatives: uncertainty about value </li></ul><ul><li>2. ‘?’ (Maybe) Annotations </li></ul><ul><li>3. Confidences </li></ul>= Three possible instances (Amy, Honda) ∥ (Amy, Toyota) ∥ (Amy, Mazda) Saw (witness,car) { Honda, Toyota, Mazda } car Amy witness
  14. 14. Our Model for Uncertainty <ul><li>1. Alternatives </li></ul><ul><li>2. ‘?’ (Maybe): uncertainty about presence </li></ul><ul><li>3. Confidences </li></ul>Six possible instances ? (Amy, Honda) ∥ (Amy, Toyota) ∥ (Amy, Mazda) (Betty, Acura) Saw (witness,car)
  15. 15. Our Model for Uncertainty <ul><li>1. Alternatives </li></ul><ul><li>2. ‘?’ (Maybe) Annotations </li></ul><ul><li>3. Confidences: weighted uncertainty </li></ul>? Six possible instances, each with a probability (Amy, Honda): 0.5 ∥ (Amy,Toyota): 0.3 ∥ (Amy, Mazda): 0.2 (Betty, Acura): 0.6 Saw (witness,car)
  16. 16. Deficiency in Model Suspects = π person (Saw ⋈ Drives) ? ? ? Does not correctly capture possible instances in the result CANNOT (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Billy, Honda) ∥ (Frank, Honda) (Hank, Honda) (Jimmy, Toyota) ∥ (Jimmy, Mazda) Drives (person,car) Jimmy Billy ∥ Frank Hank Suspects
  17. 17. Lineage to the Rescue <ul><li>Lineage (provenance): “where data came from” </li></ul><ul><ul><li>Internal lineage </li></ul></ul><ul><ul><li>External lineage </li></ul></ul><ul><li>In Trio: A function λ from alternatives to other </li></ul><ul><li>alternatives (or external sources) </li></ul>
  18. 18. Example with Lineage ? ? ? Suspects = π person (Saw ⋈ Drives) λ (31) = (11,2),(21,2) λ (32,1) = (11,1),(22,1); λ (32,2) = (11,1),(22,2) λ (33) = (11,1), 23 11 ID (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) 23 22 21 ID (Billy, Honda) ∥ (Frank, Honda) (Hank, Honda) (Jimmy, Toyota) ∥ (Jimmy, Mazda) Drives (person,car) 33 32 31 ID Jimmy Billy ∥ Frank Hank Suspects Correctly captures possible instances in the result
  19. 19. Uncertainty-Lineage Databases ( ULDBs ) <ul><li>1. Alternatives </li></ul><ul><li>2. ‘?’ (Maybe) Annotations </li></ul><ul><li>3. Confidences </li></ul><ul><li>4. Lineage </li></ul><ul><li>The ULDB model is “complete” </li></ul>
  20. 20. Querying ULDBs <ul><li>Simple extension to SQL </li></ul><ul><li>Formal semantics, intuitive meaning </li></ul><ul><li>Query uncertainty, confidences, and lineage </li></ul>TriQL
  21. 21. Initial TriQL Example SELECT Drives.person INTO Suspects FROM Saw, Drives WHERE Saw.car = Drives.car ? ? ? λ (31) = (11,2),(21,2) λ (32,1)=(11,1),(22,1); λ (32,2)=(11,1),(22,2) λ (33) = (11,1), 23 11 ID (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) 23 22 21 ID (Billy, Honda) ∥ (Frank, Honda) (Hank, Honda) (Jimmy, Toyota) ∥ (Jimmy, Mazda) Drives (person,car) 33 32 31 ID Jimmy Billy ∥ Frank Hank Suspects
  22. 22. Formal Semantics <ul><li>Query Q on ULDB D </li></ul>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 ) D’ implementation of Q operational semantics D + Result
  23. 23. TriQL: Querying Confidences <ul><li>Built-in function: Conf() </li></ul><ul><li>SELECT Drives.person INTO Suspects </li></ul><ul><li>FROM Saw, Drives </li></ul><ul><li>WHERE Saw.car = Drives.car </li></ul><ul><li>AND Conf(Saw) > 0.5 AND Conf(Drives) > 0.8 </li></ul>
  24. 24. TriQL: Querying Lineage <ul><li>Built-in join predicate: Lineage() </li></ul><ul><li>SELECT Saw.witness INTO AccusesHank </li></ul><ul><li>FROM Suspects, Saw </li></ul><ul><li>WHERE Lineage(Suspects,Saw) </li></ul><ul><li>AND Suspects.person = ‘Hank’ </li></ul>
  25. 25. Operational Semantics <ul><li>Over conventional relational database: </li></ul><ul><li>For each tuple in cross-product of X 1 , X 2 , ..., X n </li></ul><ul><ul><li>Evaluate the predicate </li></ul></ul><ul><ul><li>If true, project attr-list to create result tuple </li></ul></ul><ul><ul><li>If INTO clause, insert into table </li></ul></ul>SELECT attr-list [ INTO table ] FROM X 1 , X 2 , ..., X n WHERE predicate
  26. 26. Operational Semantics <ul><li>Over ULDB: </li></ul><ul><li>For each tuple in cross-product of X 1 , X 2 , ..., X n </li></ul><ul><ul><li>Create “super tuple” T from all combinations of alternatives </li></ul></ul><ul><ul><li>Evaluate predicate on each alternative in T ; keep only the true ones </li></ul></ul><ul><ul><li>Project attr-list on each alternative to create result tuple </li></ul></ul><ul><ul><li>Details: ‘?’, lineage, confidences </li></ul></ul>SELECT attr-list [ INTO table ] FROM X 1 , X 2 , ..., X n WHERE predicate
  27. 27. Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Hank, Honda) (Jim, Mazda) ∥ (Bill, Mazda) Drives (person,car)
  28. 28. Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Hank, Honda) (Jim, Mazda) ∥ (Bill, Mazda) Drives (person,car) (Cathy,Honda,Jim,Mazda) ∥ (Cathy,Honda,Bill,Mazda) ∥ (Cathy,Mazda,Jim,Mazda) ∥ (Cathy,Mazda,Bill,Mazda)
  29. 29. Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Hank, Honda) (Jim, Mazda) ∥ (Bill, Mazda) Drives (person,car) (Cathy,Honda,Jim,Mazda) ∥ (Cathy,Honda,Bill,Mazda) ∥ (Cathy,Mazda,Jim,Mazda) ∥ (Cathy,Mazda,Bill,Mazda)
  30. 30. Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Hank, Honda) (Jim, Mazda) ∥ (Bill, Mazda) Drives (person,car) (Cathy,Honda,Jim,Mazda) ∥ (Cathy,Honda,Bill,Mazda) ∥ (Cathy,Mazda, Jim ,Mazda) ∥ (Cathy,Mazda, Bill ,Mazda)
  31. 31. Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy,Honda,Jim,Mazda) ∥ (Cathy,Honda,Bill,Mazda) ∥ (Cathy,Mazda, Jim ,Mazda) ∥ (Cathy,Mazda, Bill ,Mazda) (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Hank, Honda) (Jim, Mazda) ∥ (Bill, Mazda) Drives (person,car) (Cathy,Honda,Hank,Honda) ∥ (Cathy,Mazda,Hank,Honda)
  32. 32. Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy,Honda,Jim,Mazda) ∥ (Cathy,Honda,Bill,Mazda) ∥ (Cathy,Mazda, Jim ,Mazda) ∥ (Cathy,Mazda, Bill ,Mazda) (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Hank, Honda) (Jim, Mazda) ∥ (Bill, Mazda) Drives (person,car) (Cathy,Honda,Hank,Honda) ∥ (Cathy,Mazda,Hank,Honda)
  33. 33. Operational Semantics: Example SELECT Drives.person FROM Saw, Drives WHERE Saw.car = Drives.car (Cathy,Honda,Jim,Mazda) ∥ (Cathy,Honda,Bill,Mazda) ∥ (Cathy,Mazda, Jim ,Mazda) ∥ (Cathy,Mazda, Bill ,Mazda) (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Hank, Honda) (Jim, Mazda) ∥ (Bill, Mazda) Drives (person,car) (Cathy,Honda, Hank ,Honda) ∥ (Cathy,Mazda,Hank,Honda)
  34. 34. Operational Semantics: Example SELECT Drives.person INTO Suspects FROM Saw, Drives WHERE Saw.car = Drives.car ? ? λ ( ) = ... λ ( ) = ... (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Hank, Honda) (Jim, Mazda) ∥ (Bill, Mazda) Drives (person,car) Jim ∥ Bill Hank Suspects
  35. 35. Confidences <ul><li>Confidences supplied with base data </li></ul><ul><li>Trio computes confidences on query results </li></ul><ul><ul><li>Default probabilistic interpretation </li></ul></ul><ul><ul><li>Can choose to plug in different arithmetic </li></ul></ul>? ? ? 0.3 0.4 0.6 Probabilistic Min (Cathy, Honda): 0.6 ∥ (Cathy, Mazda): 0.4 Saw (witness,car) (Hank, Honda) (Jim, Mazda): 0.3 ∥ (Bill, Mazda): 0.6 Drives (person,car) Jim: 0.12 ∥ Bill: 0.24 Hank: 0.6 Suspects
  36. 36. Additional Query Constructs <ul><li>“ Horizontal subqueries” </li></ul><ul><ul><li>Refer to tuple alternatives as a relation </li></ul></ul><ul><li>Unmerged (horizontal duplicates) </li></ul><ul><li>Flatten , GroupAlts </li></ul><ul><li>NoLineage , NoConf , NoMaybe </li></ul><ul><li>Query-computed confidences </li></ul><ul><li>Data modification statements </li></ul>
  37. 37. Final Example Query Credibility List suspects with conf values based on accuser credibility (1, Amy, Jimmy) ∥ (1, Betty, Billy) ∥ (1, Cathy, Hank) (2, Cathy, Frank) ∥ (2, Betty, Freddy) PrimeSuspect (crime#, accuser, suspect) Cathy Betty Amy person 15 5 10 score Jimmy: 0.33 ∥ Billy: 0.5 ∥ Hank: 0.166 Frank: 0.25 ∥ Freddy: 0.75 Suspects
  38. 38. Final Example Query Credibility SELECT suspect, score/[sum(score)] as conf FROM (SELECT suspect, (SELECT score FROM Credibility C WHERE C.person = P.accuser) FROM PrimeSuspect P) (1, Amy, Jimmy) ∥ (1, Betty, Billy) ∥ (1, Cathy, Hank) (2, Cathy, Frank) ∥ (2, Betty, Freddy) PrimeSuspect (crime#, accuser, suspect) Cathy Betty Amy person 15 5 10 score Jimmy: 0.33 ∥ Billy: 0.5 ∥ Hank: 0.166 Frank: 0.25 ∥ Freddy: 0.75 Suspects
  39. 39. Final Example Query Credibility SELECT suspect, score/[sum(score)] as conf FROM (SELECT suspect, (SELECT score FROM Credibility C WHERE C.person = P.accuser) FROM PrimeSuspect P) (1, Amy, Jimmy) ∥ (1, Betty, Billy) ∥ (1, Cathy, Hank) (2, Cathy, Frank) ∥ (2, Betty, Freddy) PrimeSuspect (crime#, accuser, suspect) Cathy Betty Amy person 15 5 10 score Jimmy: 0.33 ∥ Billy: 0.5 ∥ Hank: 0.166 Frank: 0.25 ∥ Freddy: 0.75 Suspects
  40. 40. Final Example Query Credibility SELECT suspect, score/[sum(score)] as conf FROM (SELECT suspect, (SELECT score FROM Credibility C WHERE C.person = P.accuser) FROM PrimeSuspect P) (1, Amy, Jimmy) ∥ (1, Betty, Billy) ∥ (1, Cathy, Hank) (2, Cathy, Frank) ∥ (2, Betty, Freddy) PrimeSuspect (crime#, accuser, suspect) Cathy Betty Amy person 15 5 10 score Jimmy: 0.33 ∥ Billy: 0.5 ∥ Hank: 0.166 Frank: 0.25 ∥ Freddy: 0.75 Suspects
  41. 41. Trio System: Version 1 Standard relational DBMS Trio API and translator (Python) Command-line client Trio Metadata TrioExplorer (GUI client) Trio Stored Procedures Encoded Data Tables Lineage Tables Standard SQL <ul><li>“ Verticalize” </li></ul><ul><li>Shared IDs for </li></ul><ul><li>alternatives </li></ul><ul><li>Columns for </li></ul><ul><li>confidence,“?” </li></ul><ul><li>One per result </li></ul><ul><li>table </li></ul><ul><li>Uses unique IDs </li></ul><ul><li>Table types </li></ul><ul><li>Schema-level </li></ul><ul><li>lineage structure </li></ul><ul><li>conf() </li></ul><ul><li>lineage() “==>” </li></ul><ul><li>DDL commands </li></ul><ul><li>TriQL queries </li></ul><ul><li>Schema browsing </li></ul><ul><li>Table browsing </li></ul><ul><li>Explore lineage </li></ul><ul><li>On-demand </li></ul><ul><li>confidence </li></ul><ul><li>computation </li></ul>
  42. 42. Future Features (sample) <ul><li>Uncertainty </li></ul><ul><ul><li>Incomplete relations </li></ul></ul><ul><ul><li>Continuous uncertainty </li></ul></ul><ul><ul><li>Correlated uncertainty </li></ul></ul><ul><li>Lineage </li></ul><ul><ul><li>External lineage </li></ul></ul><ul><ul><li>Update lineage </li></ul></ul><ul><li>Query processing </li></ul><ul><ul><li>“Top-K” by confidence </li></ul></ul>
  43. 43. but don’t forget the lineage… DATA UNCERTAINTY LINEAGE
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