Inferring a Relax NG Schema from XML Documents

Transcription

1 Inferring a Relax NG Schema from XML Documents Guen-Hae Kim* Sang-Ki Ko Yo-Sub Han Department of Computer Science Yonsei University 10th International Conference on Language and Automata Theory and Application Kim et al. (Yonsei University) Schema Inference LATA / 16

2 XML and Schema XML human-readable, machine-readable format author B.M.Harwani Book title genre price date C++ for beginners Computer Science $ Structural Data <Book> <author> B.M.Harwani <author/> <title> C++ for beginners <title/> <genre> Computer Science <genre/> <price> <price/> <date> 2009<date/> <Book/> XML Kim et al. (Yonsei University) Schema Inference LATA / 16

3 XML and Schema XML keep data and structure separately <Book> <author> B.M.Harwani <author/> <title> C++ for beginners <title/> <genre> Computer Science <genre/> <price> <price/> <date> 2009<date/> <Book/> XML Book author Book title genre price date Structure B.M.Harwani C++ for beginners Computer Science Data string string string double integer Kim et al. (Yonsei University) Schema Inference LATA / 16

4 XML and Schema Schema Structure for XML, checking conformity of data Book author Book title Schema (string) (string) genre (string) price (double) date (integer) XML 1 XML 2 XML 3 <Book> <title> C++ for beginners <title/> <rate> 4.12 <rate/> <date> 2009<date/> <Book/> <Book> <title> C++ for beginners <title/> <price>fourty dollars <price/> <date> 2009<date/> <Book/> <Book> <author> B.M.Harwani <author/> <title> C++ for beginners <title/> <date> 2009<date/> <Book/> Kim et al. (Yonsei University) Schema Inference LATA / 16

5 XML and Schema Schema Structure for XML, checking conformity of data Book author Book title Schema (string) (string) genre (string) price (double) date (integer) XML 1 XML 2 XML 3 <Book> <title> C++ for beginners <title/> <rate> 4.12 <rate/> <date> 2009<date/> <Book/> <Book> <title> C++ for beginners <title/> <price>fourty dollars <price/> <date> 2009<date/> <Book/> <Book> <author> B.M.Harwani <author/> <title> C++ for beginners <title/> <date> 2009<date/> <Book/> Kim et al. (Yonsei University) Schema Inference LATA / 16

6 XML and Schema Schema Structure for XML, checking conformity of data Book author Book title Schema (string) (string) genre (string) price (double) date (integer) XML 1 XML 2 XML 3 <Book> <title> C++ for beginners <title/> <rate> 4.12 <rate/> <date> 2009<date/> <Book/> <Book> <title> C++ for beginners <title/> <price> fourty dollars <price/> <date> 2009<date/> <Book/> <Book> <author> B.M.Harwani <author/> <title> C++ for beginners <title/> <date> 2009<date/> <Book/> Kim et al. (Yonsei University) Schema Inference LATA / 16

7 XML and Schema Schema Structure for XML, checking conformity of data Book author Book title Schema (string) (string) genre (string) price (double) date (integer) XML 1 XML 2 XML 3 <Book> <title> C++ for beginners <title/> <rate> 4.12 <rate/> <date> 2009<date/> <Book/> <Book> <title> C++ for beginners <title/> <price> fourty dollars <price/> <date> 2009<date/> <Book/> <Book> <author> B.M.Harwani <author/> <title> C++ for beginners <title/> <date> 2009<date/> <Book/> Kim et al. (Yonsei University) Schema Inference LATA / 16

8 Motivation Absense of Valid Schema Half of the XML documents on the web do not refer to a schema. [Barbosa et al. 05 ] XML 1 XML 2 manage & search... XML 3... XML XML 5 XML 6...? unknown schema Kim et al. (Yonsei University) Schema Inference LATA / 16

9 Previous Works 1 Inference of concise DTDs from XML Data Bex, G.J, Neven, F., Schwentick, T., Tuyls, K. In: Proceedings of the 32nd International Conference on Very Large Data Bases. VLDB Endowment (2006) <!ELEMENT store (order, stock)> <!ELEMENT order (customer, item )> <!ELEMENT customer (first, last, )> <!ELEMENT item (id, price + (qty, (supplier + item + )))> <!ELEMENT stock (item )> <!ELEMENT supplier (first, last, )> Document Type Definition(DTD) Example Kim et al. (Yonsei University) Schema Inference LATA / 16

10 Previous Works 2 Inferring XML schema definitions from XML data Bex, G.J, Neven, F., Vansummeren, S. In: Proceedings of the 33nd International Conference on Very Large Data Bases. VLDB Endowment (2007) root store order person item 1 stock item 2 emp store[store] order[order],stock[stock] customer[person],item[item 1 ] + name[emp], [emp] + id[emp],qty[emp],price[emp] item[item 2 ] + id[emp],qty[emp], (supplier[person] + item[item 2 ] + ) λ XML Schema Definition(XSD) Example Kim et al. (Yonsei University) Schema Inference LATA / 16

11 Previous Works 3 Relax NG The most expressive of the tree formalisms. [Comon, H. 07 ] League, C., Eng, K.: "Schema-based compression of XML data with RELAX NG." Journal of Computers 2(10), 9 17(2007) No existing approach for inferring Relax NG <element name= addressbook > <zeroormore> <element name= card > <text/> <element/> <element name= > <text/> <element/> <zeroormore/> <element/> RELAX NG example Kim et al. (Yonsei University) Schema Inference LATA / 16

12 Problem Definition T + : A set of positive tree instances T : A set of negative tree instances Size of schema : The number of elements and their degrees T T Relax NG schema Kim et al. (Yonsei University) Schema Inference LATA / 16

13 Problem Definition Definition INPUT : T +, T OUTPUT : Relax NG Schema G which 1 can generate instances in T + as many as possible. 2 cannot generate instances in T as many as possible. 3 has small size as much as possible. Kim et al. (Yonsei University) Schema Inference LATA / 16

14 Strategy 1 Construct a grammar for each tree in T +, and union into a sole grammar G 2 Reduce the size of G by eliminating indistinguishable variables heuristic algorithm (genetic algorithm) 3 Convert G into corresponding Relax NG schema 4 Refine schema compactly Kim et al. (Yonsei University) Schema Inference LATA / 16

15 Outline 1 Our Approach Normalized Regular Hedge Grammar Grammar Construction Learning Process by Genetic Algorithm Conversion into Schema 2 Results and Conclusion Experiment Settings Results Future Works Kim et al. (Yonsei University) Schema Inference LATA / 16

16 Normalized Regular Hedge Grammar Definition A regular hedge grammar (RHG) is a 5-tuple (Σ, X, N, P, r f ) Σ is a finite set of symbols, X is a set of variables, N is a set of non-terminals, P is a set of production rules, each of which takes one of the two forms n x, where n is a non-terminal in N, and x is a variable in X, n a r, where n is a non-terminal in N, a is a symbol in Σ, and r is a regular expression comprising non-terminals, r f is a regular expression comprising non-terminals. Kim et al. (Yonsei University) Schema Inference LATA / 16

17 Normalized Regular Hedge Grammar Example (Relax NG) element addressbook { element card { element name { text }, element { text }, element prefershtml { empty }? }* } Example (RHG) RHG G = (Σ, X, N, P, n a) where Σ = {addressbook, card, name, , prefershtml} X = {text} N = {n a, n c, n n, n e, n p} P = {n a addressbook n c n c n n(n en p n e) n n name text n e text n p prefershtml ɛ } Kim et al. (Yonsei University) Schema Inference LATA / 16

18 Normalized Regular Hedge Grammar Definition A normalized regular hedge grammar (NRHG) is a 5-tuple (Σ, V T, V F, P, s) Σ is a finite set of terminals, V T is a set of tree variables, V F is a set of forest variables, P is a set of production rules consisting of Rule 1 : T x Rule 2 : T x F Rule 3 : F T Rule 4 : F TF s V T is a start variable. Kim et al. (Yonsei University) Schema Inference LATA / 16

19 Grammar Construction Construct each NRHG for each tree in T +. Kim et al. (Yonsei University) Schema Inference LATA / 16

20 Grammar Construction Construct each NRHG for each tree in T +. a b c d Kim et al. (Yonsei University) Schema Inference LATA / 16

21 Grammar Construction Construct each NRHG for each tree in T +. a T 0 a F 0 b c d Kim et al. (Yonsei University) Schema Inference LATA / 16

22 Grammar Construction Construct each NRHG for each tree in T +. a b T 0 a F 0 F 0 T 1 c d Kim et al. (Yonsei University) Schema Inference LATA / 16

23 Grammar Construction Construct each NRHG for each tree in T +. c a b d T 0 a F 0 F 0 T 1 T 1 b F 1 Kim et al. (Yonsei University) Schema Inference LATA / 16

24 Grammar Construction Construct each NRHG for each tree in T +. c a b d T 0 a F 0 F 0 T 1 T 1 b F 1 F 1 T 2 F 2 T 2 c F 2 T 3 T 3 d Kim et al. (Yonsei University) Schema Inference LATA / 16

25 Grammar Construction Find and merge right-mergable variables a T 0 a F 0 F 0 T 1 a T 6 a F 5 F 5 T 7 b T 1 b F 1 F 1 T 2 b T 7 b F 6 F 6 T 8 c T 2 c F 2 F 2 T 3 k T 8 k F 7 F 7 T 9 d T 3 d F 3 F 3 T 4F 4 d T 9 d F 8 F 8 T 10F 9 e f T 4 e F 4 T 5 T 5 f e f T 10 e F 9 T 11 T 11 f Kim et al. (Yonsei University) Schema Inference LATA / 16

26 Grammar Construction Find and merge right-mergable variables a T 0 a F 0 F 0 T 1 a T 6 a F 5 F 5 T 7 b T 1 b F 1 F 1 T 2 b T 7 b F 6 F 6 T 8 c T 2 c F 2 F 2 T 3 k T 8 k F 7 F 7 T 9 d T 3 d F 3 F 3 T 4F 4 d T 9 d F 8 F 8 T 10F 9 e f T 4 e F 4 T 5 T 5 f e f T 10 e F 9 T 11 T 11 f Kim et al. (Yonsei University) Schema Inference LATA / 16

27 Grammar Construction Find and merge right-mergable variables a T 0 a F 0 F 0 T 1 a T 6 a F 5 F 5 T 7 b T 1 b F 1 F 1 T 2 b T 7 b F 6 F 6 T 8 c T 2 c F 2 F 2 T 3 k T 8 k F 7 F 7 T 9 d T 3 d F 3 F 3 T 4F 4 d T 9 d F 8 F 8 T 10F 9 e f T 4 e F 4 T 5 T 5 f e f T 10 e F 9 T 11 T 11 f T 5 Kim et al. (Yonsei University) Schema Inference LATA / 16

28 Grammar Construction Find and merge right-mergable variables a T 0 a F 0 F 0 T 1 a T 6 a F 5 F 5 T 7 b T 1 b F 1 F 1 T 2 b T 7 b F 6 F 6 T 8 c T 2 c F 2 F 2 T 3 k T 8 k F 7 F 7 T 9 d T 3 d F 3 F 3 T 4F 4 d T 9 d F 8 F 8 T 10F 9 e f T 4 e F 4 T 5 T 5 f e f T 10 e F 9 T 5 T 11 f Kim et al. (Yonsei University) Schema Inference LATA / 16

29 Grammar Construction Find and merge right-mergable variables a T 0 a F 0 F 0 T 1 a T 6 a F 5 F 5 T 7 b T 1 b F 1 F 1 T 2 b T 7 b F 6 F 6 T 8 c T 2 c F 2 F 2 T 3 k T 8 k F 2 F 7 T 9 d T 3 d F 3 F 3 T 4F 4 d T 9 d F 8 F 8 T 10F 9 e f T 4 e F 4 T 5 T 5 f e f T 10 e F 9 T 11 T 11 f Kim et al. (Yonsei University) Schema Inference LATA / 16

30 Grammar Construction Find and merge left-mergable variables a T 0 a F 0 F 0 T 1 a T 0 T 6 a F 5 F 5 T 7 b T 1 b F 1 F 1 T 2 b T 7 b F 6 F 6 T 8 c T 2 c F 2 F 2 T 3 k T 8 k F 2 F 7 T 9 d T 3 d F 3 F 3 T 4F 4 d T 9 d F 8 F 8 T 10F 9 e f T 4 e F 4 T 5 T 5 f e f T 10 e F 9 T 11 T 11 f Kim et al. (Yonsei University) Schema Inference LATA / 16

31 Grammar Construction Find and merge left-mergable variables a T 0 a F 0 F 0 T 1 a T 0 a F 5 F 5 T 7 b T 1 b F 1 F 1 T 2 b T 7 b F 6 F 6 T 8 c T 2 c F 2 F 2 T 3 k T 8 k F 2 F 7 T 9 d T 3 d F 3 F 3 T 4F 4 d T 9 d F 8 F 8 T 10F 9 e f T 4 e F 4 T 5 T 5 f e f T 10 e F 9 T 11 T 11 f Kim et al. (Yonsei University) Schema Inference LATA / 16

32 Grammar Construction Find and merge left-mergable variables a T 0 a F 0 F 0 T 1 a F 0 T 6 a F 5 F 5 T 7 b T 1 b F 1 F 1 T 2 b T 7 b F 6 F 6 T 8 c T 2 c F 2 F 2 T 3 k T 8 k F 2 F 7 T 9 d T 3 d F 3 F 3 T 4F 4 d T 9 d F 8 F 8 T 10F 9 e f T 4 e F 4 T 5 T 5 f e f T 10 e F 9 T 11 T 11 f Kim et al. (Yonsei University) Schema Inference LATA / 16

33 Grammar Construction Find and merge left-mergable variables a T 0 a F 0 F 0 T 1 a T 6 a F 5 F 5 T 7 b T 1 b F 1 F 1 T 2 b T 7 b F 6 F 6 T 8 c T 2 c F 2 F 2 T 3 k T 2 k F 2 F 7 T 9 d T 3 d F 3 F 3 T 4F 4 d T 9 d F 8 F 8 T 10F 9 e f T 4 e F 4 T 5 T 5 f e f T 10 e F 9 T 11 T 11 f Kim et al. (Yonsei University) Schema Inference LATA / 16

34 Learning Process by Genetic Algorithm Individual p a T 0 a F 0 T 2 c b F 0 T 1 T 1 b F 1 F 2 T 3 T 3 d T = {T 0, T 1, T 2, T 3 } c d F 1 T 2 F 2 p = 1234 {1}{2}{3}{4} Kim et al. (Yonsei University) Schema Inference LATA / 16

35 Learning Process by Genetic Algorithm 1 Generate 1000 individuals from NRHG T = {T 1, T 2,..., T n } p 1 = n p 2 = n... p 1000 = n Kim et al. (Yonsei University) Schema Inference LATA / 16

36 Learning Process by Genetic Algorithm 2 Apply genetic operators (example) c a b d T 0 a F 0 F 0 T 1 T 1 b F 1 F 1 T 2 F 2 T 2 c F 2 T 3 T 3 d p = 1234 p = 1233 Kim et al. (Yonsei University) Schema Inference LATA / 16

37 Learning Process by Genetic Algorithm 2 Apply genetic operators (example) a T 0 a F 0 T 2 c a b F 0 T 1 T 1 b F 1 F 2 T 3 T 2 d b c d F 1 T 2 F 2 c c p = 1234 T 3 T 2 p = 1233 T = {T 0, T 1, T 2, T 3 } T = {T 0, T 1, T 2, T 2 } {1}{2}{3}{4} {1}{2}{3, 4} Kim et al. (Yonsei University) Schema Inference LATA / 16

38 Learning Process by Genetic Algorithm 2 Apply genetic operators : Crossover p 3 = 1234 {1}{2}{3}{4} p 5 = 1234 {1}{2}{3}{4} Kim et al. (Yonsei University) Schema Inference LATA / 16

39 Learning Process by Genetic Algorithm 2 Apply genetic operators : Crossover p 3 = 1234 {1}{2}{3}{4} p 5 = 1234 {1}{2}{3}{4} Kim et al. (Yonsei University) Schema Inference LATA / 16

40 Learning Process by Genetic Algorithm 2 Apply genetic operators : Crossover p 3 = 1234 {1}{2}{3}{4} p 5 = 1234 {1}{2}{3}{4} p 3 = 1232 {1}{2, 4}{3} p 5 = 1434 {1} {3}{2, 4} Kim et al. (Yonsei University) Schema Inference LATA / 16

41 Learning Process by Genetic Algorithm 2 Apply genetic operators : Mutation p 13 = 1434 Kim et al. (Yonsei University) Schema Inference LATA / 16

42 Learning Process by Genetic Algorithm 2 Apply genetic operators : Mutation p 13 = p 13 = 1444 Kim et al. (Yonsei University) Schema Inference LATA / 16

43 Learning Process by Genetic Algorithm 2 Apply genetic operators : Crossover(0.3) Mutation(0.1) p 1 = n p 2 = n... p 1000 = n 300 Crossover then 100 Mutation p 1 = n p 2 = n... p 1000 = n Kim et al. (Yonsei University) Schema Inference LATA / 16

44 Learning Process by Genetic Algorithm 3 Score each individual according to fitness function f (if T L(G) = ) 1 f(p i ) = V F + V T + 1 P + {w T + w L(G)} T + L(G) : Language of p i V F, V T : The number of Forest and Tree Variables V P : The number of Production Rules Kim et al. (Yonsei University) Schema Inference LATA / 16

45 Learning Process by Genetic Algorithm 4 Generate new 1000 individuals by Roulette-wheel selection p 1 = n p 2 = n... p 1000 = n Sort p 53 = n p 171 = n... p 555 = n f(p i ) Kim et al. (Yonsei University) Schema Inference LATA / 16

46 Learning Process by Genetic Algorithm 4 Generate new 1000 individuals by Roulette-wheel selection f(p 53) p p 171 f(p 171) P (p i ) = f(p i) k f(p k) Roulette-wheel Selection Kim et al. (Yonsei University) Schema Inference LATA / 16

47 Learning Process by Genetic Algorithm 4 Generate new 1000 individuals by Roulette-wheel selection p 53 = n p 171 = n... p 555 = n Keep best 10% p 1 = n p 2 = n... p 1000 = n Kim et al. (Yonsei University) Schema Inference LATA / 16

48 Learning Process by Genetic Algorithm 4 Generate new 1000 individuals by Roulette-wheel selection p 53 = n p 171 = n... p 555 = n Keep best 10% 90% Roulette Wheel p 1 = n p 2 = n... p 1000 = n Kim et al. (Yonsei University) Schema Inference LATA / 16

49 Rules of NRHG Rule 1 : T x Rule 2 : T x F Rule 3 : F T Rule 4 : F TF Kim et al. (Yonsei University) Schema Inference LATA / 16

50 Conversion into Schema 1 Rule 1 (T x) Conversion T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record Kim et al. (Yonsei University) Schema Inference LATA / 16

51 Conversion into Schema 1 Rule 1 (T x) Conversion T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record <define name=t 2 > <element name=country> <text/> <element/> <define/> <define name=t 3 > <element name=record> <text/> <element/> <define/> Kim et al. (Yonsei University) Schema Inference LATA / 16

52 Conversion into Schema 2 Rule 2 (T x F ) Conversion T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record Kim et al. (Yonsei University) Schema Inference LATA / 16

53 Conversion into Schema 2 Rule 2 (T x F ) Conversion T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record <define name=t 0 > <element name=records> <ref name=f 0 /> <element/> <define/> <define name=t 1 > <element name=car> <ref name=f 1 /> <element/> <define/> Kim et al. (Yonsei University) Schema Inference LATA / 16

54 Conversion into Schema Hardness of Rule 3,4 Conversion T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record <define name=f 0 > <group> <ref name=t 1 /> <ref name=f 0 /> <group/> <define/> Kim et al. (Yonsei University) Schema Inference LATA / 16

55 Conversion into Schema 3 Rule 3,4 Conversion (NRHG NFA) T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record Kim et al. (Yonsei University) Schema Inference LATA / 16

56 Conversion into Schema 3 Rule 3,4 Conversion (NRHG NFA) T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record NFA A F0 F 0 Kim et al. (Yonsei University) Schema Inference LATA / 16

57 Conversion into Schema 3 Rule 3,4 Conversion (NRHG NFA) T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record NFA A F0 T 1 F 0 Kim et al. (Yonsei University) Schema Inference LATA / 16

58 Conversion into Schema 3 Rule 3,4 Conversion (NRHG NFA) T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record T 1 NFA A F0 T F 1 0 f Kim et al. (Yonsei University) Schema Inference LATA / 16

59 Conversion into Schema 3 Rule 3,4 Conversion (NRHG NFA) T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record T 1 NFA A F0 T F 1 0 f NFA A F1 F 1 Kim et al. (Yonsei University) Schema Inference LATA / 16

60 Conversion into Schema 3 Rule 3,4 Conversion (NRHG NFA) T 0 records F 0, T 1 car F 1, F 1 T 2F 2, F 0 T 1F 0, F 0 T 1, F 2 T 3, T 2 country, T 3 record T 1 NFA A F0 T F 1 0 f NFA A F1 T F 2 1 F 2 T 3 f Kim et al. (Yonsei University) Schema Inference LATA / 16

61 Conversion into Schema 3 Rule 3,4 Conversion (NFA Regular Expression) T 1 NFA A F0 T F 1 0 f NFA A F1 T F 2 1 F 2 T 3 f Kim et al. (Yonsei University) Schema Inference LATA / 16

62 Conversion into Schema 3 Rule 3,4 Conversion (NFA Regular Expression) T 1 NFA A F0 T F 1 0 f R F0 = T 1 T 1 NFA A F1 T F 2 1 F 2 T 3 f R F1 = T 2 T 3 Kim et al. (Yonsei University) Schema Inference LATA / 16

63 Conversion into Schema 3 Rule 3,4 Conversion (Regular Expression Relax NG) R F0 = T 1 T 1 Kim et al. (Yonsei University) Schema Inference LATA / 16

64 Conversion into Schema 3 Rule 3,4 Conversion (Regular Expression Relax NG) R F0 = T 1 T 1 <define name=f 0 > <group> <zeroormore> <ref name=t 1 /> <zeroormore/> <ref name=t 1 /> <group/> <define/> Kim et al. (Yonsei University) Schema Inference LATA / 16

65 Conversion into Schema 3 Rule 3,4 Conversion (Regular Expression Relax NG) R F1 = T 2 T 3 <define name=f 1 > <group> <ref name=t 2 /> <ref name=t 3 /> <group/> <define/> Kim et al. (Yonsei University) Schema Inference LATA / 16

66 Conversion into Schema 3 Rule 3,4 Conversion (Regular Expression Relax NG) R F1 = T 2 + T 3 <define name=f 1 > <choice> <ref name=t 2 /> <ref name=t 3 /> <choice/> <define/> Kim et al. (Yonsei University) Schema Inference LATA / 16

67 Conversion into Schema 4 Refine Schema (Kleene Plus) R F0 = T 1 T 1 R F0 = T + 1 <define name=f 0 > <group> <zeroormore> <ref name=t 1 /> <zeroormore/> <ref name=t 1 /> <group/> <define/> Kim et al. (Yonsei University) Schema Inference LATA / 16

68 Conversion into Schema 4 Refine Schema (Kleene Plus) R F0 = T 1 T 1 <define name=f 0 > <group> <zeroormore> <ref name=t 1 /> <zeroormore/> <ref name=t 1 /> <group/> <define/> R F0 = T + 1 <define name=f 0 > <oneormore> <ref name=t 1 /> <oneormore/> <define/> Kim et al. (Yonsei University) Schema Inference LATA / 16

69 Conversion into Schema 4 Refine Schema (Redundancy) T 1 a F 1 T 2 b F 2 F 1 T 3 F 2 F 2 T 4 F 3... NFA A F1 NFA A F2 F 1 T 3 F 2 T 4 F3... F 2 T 4 F3... Kim et al. (Yonsei University) Schema Inference LATA / 16

70 Conversion into Schema 4 Refine Schema (Redundancy) T 1 a F 1 T 2 b F 2 F 1 T 3 F 2 F 2 T 4 F 3... NFA A F1 NFA A F2 F 1 T 3 F 2 T 4 F3... F 2 T 4 F3... R F1 = T 3 T 4... R F2 = T 4... Kim et al. (Yonsei University) Schema Inference LATA / 16

71 Conversion into Schema 4 Refine Schema (Redundancy) T 1 a F 1 T 2 b F 2 F 1 T 3 F 2 F 2 T 4 F 3... NFA A F1 NFA A F2 F 1 T 3 F 2 T 4 F3... F 2 T 4 F3... F 2 R F1 = T 3 T 4... R F2 = T 4... Kim et al. (Yonsei University) Schema Inference LATA / 16

72 Experiment Settings xmlgen : random tree generation from given Relax NG schema 1 Input data : 50 positive trees, 25 negative trees ( 100 error rate) Benchmark Relax NG : XML-DSig, XENC, IBTWSH Restricting Points Ignore description of attributes Omit anyname Restrict zeroormore iteration to 2. Benchmark Schema T + = 1000 T = 1000 T + = 25 T = Our Approach Validation Result Schema Kim et al. (Yonsei University) Schema Inference LATA / 16

73 Results Reduction of the Schema Size Kim et al. (Yonsei University) Schema Inference LATA / 16

74 Results Precision of the Schema Kim et al. (Yonsei University) Schema Inference LATA / 16

75 Future Works Future Works Efficient learning process Shorter regular expression Other Relax NG specifications Kim et al. (Yonsei University) Schema Inference LATA / 16

76 Thank You!! Kim et al. (Yonsei University) Schema Inference LATA / 16