Fuzzy Hash Map
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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.
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Fuzzy Hash Map
- 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
- 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
- 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