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Learning Semantic String Transformations from Examples

Learning Semantic String Transformations from Examples. Rishabh Singh and Sumit Gulwani. FlashFill. Transformations. Syntactic Transformations Concatenation of regular expression based substring “VLDB2012”  “VLDB” Semantic Transformations More than just characters

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Learning Semantic String Transformations from Examples

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  1. Learning Semantic String Transformations from Examples Rishabh Singh and SumitGulwani

  2. FlashFill

  3. Transformations • Syntactic Transformations • Concatenation of regular expression based substring • “VLDB2012”  “VLDB” • Semantic Transformations • More than just characters • “1/5/2010”  “May 1st 2010”

  4. Semantic Transformations • Semantic information as relational tables • 1  January, 2  February • Learn table lookup queries • VLOOKUP macro 2nd most problematic

  5. Outline • Lookup Transformations • Lookup + Syntactic Transformations • Case Studies

  6. Demo Table Lookup Transformations

  7. Learning Framework Input Strings Output String F … F1 Fn 1. Domain-specific Language L 2. Algorithm to learn all Fs from (i,o)

  8. Lookup Transformation Language

  9. Example - Lookup Select(Name, EmpRecord, (SSN = v1))

  10. Example – Transitive Lookup Select(Price, PriceRec, (ItemId = Select(ItemId, ItemRec, Item = v1))

  11. Learn Query Select(Price, PriceRec, (ItemId = Select(ItemId, ItemRec, Item = v1))

  12. Synthesis Algorithm : • Input: (input state , output string ) • Output: all conforming expressions • Reachability algorithm from input strings

  13. Strings reachable from input row

  14. ) strings in table rows of visited nodes

  15. …….. Repeat until k steps or fixpoint

  16. ……..

  17. Sound and k-complete • t: number of reachable strings • p: number of candidate keys • m: maximum size of a candidate key

  18. Data structure • Maintains tree structure • share common sub-expressions • CNF of Boolean Conditionals • independent column predicates

  19. Synthesize Procedure Synthesize((i1,o1), …, (in,on)) P = GenerateStrt(i1,o1) for j = 2 to n: P’ = GenerateStrt(ij,oj) P = Intersectt(P’, P) return P

  20. Demo Semantic String Transformations

  21. Syntactic String Language [GulwaniPOPL11]

  22. Combined Language Syntactic manipulations over lookup outputs Syntactic manipulations before indexing

  23. Synthesis Algorithm: • Reachability based on syntactic string matches • Boolean conditionals

  24. { “SSN: 044-58-3429”, “044-58-3429”, “1125”, “Steve Russell” } Set of reachable strings

  25. { “SSN: 044-58-3429”, “044-58-3429”, “1125”, “Steve Russell” } and in paper

  26. Experiments • 50 benchmark problems • 12 , 38 • ~1020 consistent expressions • Size of data structure: ~2000 • Performance: 96% less than 1 second • Ranking: at most 3 examples (95% 2 examples)

  27. Related Work • Matching strings for table joins • Record Matching [Elmagarmid et. al. 07, Koudas et. al. SIGMOD06] • Schema Matching [Dhamankar et. al. SIGMOD04, Warren & Tompa VLDB06] • Query Synthesis • from representative view [Das Sharma et.al. ICDT10, Tran et.al. SIGMOD09] • Text-editing by example • QuickCode[Gulwani POPL11] • SMARTedit[Lau et.al. ML03], Simulatenous Editing[Miller et.al. USENIX01]

  28. Thanks! Algorithm Designers Software Developers End-Users Large potential

  29. Backup slides

  30. Semantic String Transformations =TEXT(C,”00 00”)+0

  31. Semantic String Transformations

  32. Idea 1: Share sub-expressions e Select(C2, T1, C1=v1) Select(C3, T2, C1=e) Select(C2, T3, C1=Select(C2,T2,C1=e)

  33. Youtube Videos French Polish Urdu German Serbian Russian http://bit.ly/flashfill

  34. Idea 2: CNF conditionals

  35. No. of Consistent Expressions

  36. Succinct Representation

  37. Performance

  38. Ranking

  39. Idea 2: CNF conditionals

  40. : string value : set of lookup programs to generate

  41. Related Work • Record Matching • Similarity functions for matching [Elmagarmid et. al. 07, Koudas et. al. SIGMOD06] • Customizable similarity function [Arasu et. al. VLDB09] • Learning Schema Matches • iMAP [Dhamankar et. al. SIGMOD04] concat. of column strings using domain-specific knowledge • [Warren & Tompa VLDB06] concatenation of column substrings, single table

  42. Related Work • Query Synthesis [Das Sharma et.al. ICDT10, Tran et.al. SIGMOD09] • Infer relation from large representative example view • no joins or projections • Text-editing using examples • QuickCode[Gulwani POPL11] string transformations • SMARTedit[Lau et.al. ML03], Simulatenous Editing[Miller et.al. USENIX01] programming by demonstration

  43. General Framework • A Domain-specific Transformation Language L • Expressive and succinct • Efficient Data structures for set of expressions • Version-space algebra • GenerateStr • All sets of expressions from I-O example • Intersect • Intersect two sets of expressions

  44. Example - Lookup Select(Name, EmpRecord, (SSN = v1))

  45. Example – Transitive Lookups Select(Price, PriceRec, (ItemId = Select(ItemId, ItemRec, Item = v1))

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