This is a presentation of Fuzzy Hash Map (FHM). FHM is an extension to the regular Java HashMap data structure allowing efficient fuzzy string key search. Customizable algorithms and settings bring flexibility to this new data structure, making it adaptable to each specific use case. Fuzzy string search performance comparison between Fuzzy Hash Map and the regular HashMap are presented for both accuracy and time consumption. Results show very good performance for Fuzzy Hash Map compared to the regular HashMap.

1. Efficient Fuzzy Search Enabled Hash Map Vasile Topac PhD Student Department of Information Technology and Computer Science “Politehnica” University Of Timisoara Email: vasile.topac@aut.upt.ro 4th International Workshop On Soft Computing Applications SOFA2010 – Arad, ROMANIA

2. How it all started & SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

4. stores (key, value) pairs

5. search by key

6. very fast

7. a hash function generates a hash code for indexation

8. Uses equals method to compare trough the keys

9. only values for existing keyscan be retrieved SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

10. Java HashMap phone book example SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

11. Java HashMap Collision SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

12. Java HashMap Search for “Lisa Smith” hashMap.get(“Lisa Smith”); Result: “521-8976” SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

15. Fuzzy Hash Map “ Soft computing (SC) is a collection of methodologies that are trying to cope with the main disadvantage of the conventional (hard) computing: the poor performances when working in uncertain conditions. ” SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

16. Fuzzy Hash Map UML Class Diagram SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

18. prehashing- create collisions to cluster data

19. substring substring(“Fuzzy Search”, 0, 4) = “Fuzz”

20. soundexsoundex(“Fuzzy Search”) = F226

21. equals(Object o)

22. string metrics

23. Levenshtain DistanceLD(computing, computation)=4

24. Hamming DistanceHD(computing, computers)=3SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

26. prehashing

27. substring 4

28. equals(Object o)

29. Levenshtain DistanceSOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

31. Fuzzy Hash Map fuzzyHashMap.getFuzzy(“violent”) = “violence” LD(violent, violence) = 2 LD(violent, violation) = 5 “violence” is returned SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

33. prehashing

34. soundex

35. equals(Object o)

36. Levenshtain DistanceSOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

39. text of 568 words

40. 43 words identified as medical terms

41. 9 were incorrect matches

42. 80% accuracy

43. Parse text from eMedicine web site

44. text of 2730 words

45. 260 were recognized

46. 7 were incorrect matches

47. 97% accuracySOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

52. Flexible (can choose different pre-hashing functions and string metrics)

53. available as open source http://fuzzyhashmap.sourceforge.net/

54. community can extend the functionality

55. Future work:

56. adding more string metrics

57. improve performance

58. implement Fuzzy TreeMapSOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac

59. Thank you! sources at: http://fuzzyhashmap.sourceforge.net contact vasile.topac@aut.upt.ro SOFA2010 – Arad, ROMANIA - Efficient Fuzzy Search Enabled Hash Map - VasileTopac