1. Enriching Traditional Databases with Fuzzy Definitions to Allow Flexible and Expressive Searches
Victor Pablos-Ceruelo
Susana Muñoz-Hernández
Universidad Polité́cnica de Madrid, Spain

2. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

3. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

4. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

5. Available
Crisp data
price, age, temperature, distance, ...

6. Available Crisp data (update and maintenance)
price, age, temperature, distance, ...

7. Available Crisp data (update and maintenance)
price, age, temperature, distance, ...
Crisp queries
price < 200, temperature > 25, film_type=comedy, …

8. Available Crisp data (update and maintenance)
price, age, temperature, distance, ...
Crisp queries (limited)
price < 200, temperature > 25, film_type=comedy, …

9. Real World Crisp data (update and maintenance)
price, age, temperature, distance, ...
Crisp queries (limited)
price < 200, temperature > 25, film_type=comedy, …
Fuzzy data
cheap, expensive, fast, warm, cold, …

10. Real World Crisp data (update and maintenance)
price, age, temperature, distance, ...
Crisp queries (limited)
price < 200, temperature > 25, film_type=comedy, …
Fuzzy data (subjective)
cheap, expensive, fast, warm, cold, …

11. Real World Crisp data (update and maintenance)
price, age, temperature, distance, ...
Crisp queries (limited)
price < 200, temperature > 25, film_type=comedy, …
Fuzzy data (subjective)
cheap, expensive, fast, warm, cold, …
Fuzzy queries
very cheap, not expensive, quite old, ...

12. Real World Crisp data (update and maintenance)
price, age, temperature, distance, ...
Crisp queries (limited)
price < 200, temperature > 25, film_type=comedy, …
Fuzzy data (subjective)
cheap, expensive, fast, warm, cold, …
Fuzzy queries (expressive)
very cheap, not expensive, quite old, ...

13. Desirable Crisp data (update and maintenance)
price, age, temperature, distance, ...
Crisp queries (limited)
price < 200, temperature > 25, film_type=comedy, …
Fuzzy world (subjective)
cheap, expensive, fast, warm, cold, …
Fuzzy queries (expressive)
very cheap, not expensive, quite old, ...

14. Motivation Need to fill the gap between
the crisp data (at most available databases) and
the desirable fuzzy queries (natural at human mind of databases users)

15. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

16. Goal Provide for expressive queries: Syntax and semantics
Search engine over crisp databases
Automatic tools for databases owners to add fuzzy criteria about their data
Generic web interface prototype for searching databases

17. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

18. From Crisp till Fuzzy data

19. From Crisp till Fuzzy data

20. Fuzzification of crisp data

21. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

22. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

23. FleSe: Flexible Searches
FleSe is a framework that allows
database owners
to define fuzzy search criteria over their data
database users
to perform fuzzy queries in traditional crisp databases
FleSe offers
Web interface
Parametric database selection
Personalization of fuzzy search criteria

24. Technical details Tomcat server behind an Apache proxy
Prolog database (plain text)
Java interface
Ciao Prolog System (free sw framework)
RFuzzy package (over CLP(R))

25. RFuzzy Library Models Multi adjoint Logic
Over Prolog (distributed computation, constraints, finite domains, ...)
Sugar Syntax (fuzzy functions defs)
Similarity
Types
Combine crisp and fuzzy information
Incomplete information (default values, conditioned)

26. Extra expressive features
Fencing modifiers
Negation
Personalized concepts
Overloaded concepts

27. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

28. Queries examples

29. Query example

30. Query elements individuals comparison operators fuzzy concepts
(restaurant, film, house, ...)
comparison operators
(equal, distinct, greater, less, similar, ...)
fuzzy concepts
(big, cheap, close to the beach, ...)
fencer modifiers
(quite, rather, much, very, little, ...)
crisp concepts
(prize, size, distance, food type, ...)
values
(30000, 3, mediterranean, comedy, ...)

31. Query syntax

32. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

33. Generic engine

34. Table selection

35. Crisp and Fuzzy concepts

36. Multi-criteria search

37. Results

38. Outline Introduction Motivation Goal Database Fuzzification Approach
FleSe tool
Flexible Queries Syntax
Friendly generic interface
Conclusions

39. Conclusions Query syntax Search flexible engine Prototype: FleSe tool
Over traditional crisp databases
Search flexible engine
Based on constraints
Prototype: FleSe tool
User friendly interface
General
Personalized concepts

40. Conclusions Query syntax Search flexible engine Prototype: FleSe tool
Over traditional crisp databases
Search flexible engine
Based on constraints
Prototype: FleSe tool
User friendly interface
General
Personalized concepts
Serious attempt for feeling the gap to get expressive flexible searches

41. Enriching Traditional Databases with Fuzzy Definitions to Allow Flexible and Expressive Searches
Victor Pablos-Ceruelo
Susana Muñoz-Hernández
Universidad Polité́cnica de Madrid, Spain