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Improved Duplicate Record Detection Using ASCII Code Q-gram Indexing Technique

  • Mayada A. Elziky
  • Dina M. Ibrahim
  • Amany M. Sarhan
Research Article - Computer Engineering and Computer Science
  • 40 Downloads

Abstract

With the aim of reducing duplicate records in databases, duplicate record detection (DRD) ensures the integrity of data. Its role is to identify records signifying same entities either in the same or in different compared to database. A diversity of indexing techniques has been proposed to support DRD. Q-gram is one of the common techniques used to index databases. This paper introduces modification to the Q-gram indexing technique. Such modification participates in improving the performance of the duplicate detection process and in reducing the time and number of comparisons. In the proposed work, in order to make the back-end computations easier, Q-gram strings are alternatively converted into numeric values using their corresponding ASCII code. Based on these numeric values, the indexing will decrease the complexity of Q-gram comparisons and speed up the DRD process as a whole. Unlike the existing approaches, the proposed technique is easier in implementation and requires less memory space. Two other variations of the proposed technique are introduced in this paper to decrease the matching process time; the first uses a range for matching, while the second sorts words alphabetically inside blocks. According to experimental results, the three proposed techniques perform much faster and are almost as accurate as the current Q-gram technique, meaning that they can be used in large-sized databases DRD.

Keywords

Duplicate record detection Q-gram Indexing technique BKV ASCII code 

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References

  1. 1.
    Issa, H.: Application of Duplicate Records Detection Techniques to Duplicate Payments in a Real Business Environment. Rutgers University, Rutgers Business School (2010)Google Scholar
  2. 2.
    Naderi, H.; Salehpour, N.; Farokhi, M.N.; Chegeni, B.H.: The search of new issues in the detection of near-duplicated documents. Int. J. Curr. Rev. 2(2), 25–34 (2014)Google Scholar
  3. 3.
    Christen, P.: A survey of indexing techniques for scalable record linkage and deduplication. IEEE Trans. Knowl. Data Eng. 24, 1537–1555 (2012)CrossRefGoogle Scholar
  4. 4.
    Fellegi, I.P.; Sunter, A.B.: A theory for record linkage. J. Am. Stat. Soc. 64(328), 1183–1210 (1969)CrossRefMATHGoogle Scholar
  5. 5.
    Hernandez, M.A.; Stolfo, S.J.: The merge/purge problem for large databases. In: Proceedings of the ACM SIGMOD’95, San Jose (1995)Google Scholar
  6. 6.
    Aizawa, A.; Oyama, K.: A fast linkage detection scheme for multi-source information integration. In: Proceedings of the IEEE International Workshop on Challenges in Web Information Retrieval and Integration WIRI’05, Tokyo, Japan (2005)Google Scholar
  7. 7.
    Cohen, W.W.; Richman, J.: Learning to Match and cluster large high-dimensional data sets for data integration. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACMSIGKDD’02, Edmonton, pp. 475–480 (2002)Google Scholar
  8. 8.
    Gravano, L.; Ipeirotis, P.G.; Jagadish, H.V.; Koudas, N.; Muthukrishnan, S.; Srivastava. D.: Approximate string joins in a database (Almost) for free. VLDB (2001)Google Scholar
  9. 9.
    Adrian, B.; Christian, B.; Sean, R.; Rainer, S.: High quality linkage using multibit trees for privacy-preserving blocking. Int. J. Popul. Data Sci. (IJPDS) 1(1), 130 (2016)Google Scholar
  10. 10.
    Kevin, Z.; Peter, A.: A Q-gram birthmarking approach to predicting reusable hardware. In: Design, automation & test in Europe conference and exhibition (DATE), 14–18 March (2016)Google Scholar
  11. 11.
    Jie, L.; Haiying, Z.: Research and implementation of finding duplicate science project based on dimension filtering of Q-gram index. Destech Transactions on Engineering and Technology Research (2016)Google Scholar
  12. 12.
    Christen, P.: FEBRL: An open source data cleaning, deduplication and record linkage system with a graphical user interface. In: Proceeding of the 14th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD’08), Las Vegas, USA, pp. 1065–1068, Aug. 24–27 (2008)Google Scholar
  13. 13.
    Elmagarmid, A.K.: Duplicate record detection: a survey. IEEE Trans. Knowl. Data Eng. 19(1), 1–16 (2007)CrossRefGoogle Scholar
  14. 14.
    Alnoory, M.K.: Performance evaluation of similarity functions for duplicate record detection. M.Sc. Thesis, Yarmouk University (2011)Google Scholar
  15. 15.
    Churches, T.; Christen, P.; Lim, K.; Zhu, J.X.: Preparation of name and address data for record linkage using hidden Markov models. BioMed Cent. Med. Inf. Decis. Mak. 2(1), 9 (2002)CrossRefGoogle Scholar
  16. 16.
    Rahm, E.; Do, H.H.: Data cleaning: problems and current approaches. IEEE Data Eng. Bull. 23(4), 3–13 (2000)Google Scholar
  17. 17.
    Bilenko, M.; Mooney, R.J.: On evaluation and training set construction for duplicate detection. In: Proceedings of the ACM SIGKDD’03 Workshop on Data Cleaning, Record Linkage and Object Consolidation, Washington, DC, pp. 7–12 (2003)Google Scholar
  18. 18.
    Higazy, A.A.; Sarhan, A.M.; El Tobely, T.: Web-based Arabic/English duplicate record detection with nested blocking technique. In: Proceedings of the IEEE 8th International Conference on Computer Engineering and Systems (ICCES), Egypt, pp. 313–318 (2013)Google Scholar
  19. 19.
    Azman, S.: Efficient identity matching using static pruning Q-gram indexing approach. Decis. Support Syst. 73, 97–108 (2015)CrossRefGoogle Scholar
  20. 20.
    Ramadan, B.; Christen, P.: Unsupervised blocking key selection for real-time entity resolution. In: Advances in Knowledge Discovery and Data Mining Volume 9078 of the Series, Lecture Notes in Computer Science. Springer, pp. 574–585 (2015)Google Scholar
  21. 21.
    Kreft, S.; Navarro, G.: On compressing and indexing repetitive sequences. Theor. Comput. Sci. J. 483, 115–133 (2013)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    McCallum, A.; Nigam, K.; Ungar, L.H.: Efficient clustering of high-dimensional datasets with application to reference matching. In: Proceedings of the ACM International Conference Knowledge Discovery and Data Mining, ACM SIGKDD’00, Boston, pp. 169–178 (2000)Google Scholar
  23. 23.
    Christen, P.: A comparison of personal name matching: techniques and practical issues. In: Proceedings of the IEEE Workshop on Mining Complex Data, IEEE ICDM’06, Hong Kong (2006)Google Scholar
  24. 24.
    Kumar, A.; Ingle, Y.S.; Pande, A.; Dhule, P.: Canopy clustering: a review on pre-clustering approach to K-means clustering. Int. J. Innov. Adv. Comput. Sci. (IJIACS) 3(5), 22–29 (2014)Google Scholar
  25. 25.
    Cohen, W.W.; Ravikumar, P.; Fienberg, S.: A comparison of string distance metrics for name-matching tasks. In: Proceedings of the Workshop on Information Integration on the Web, held at IJCAI’03, Acapulco (2003)Google Scholar
  26. 26.
    Christen, P.; Goiser, K.: Quality and complexity measures for data linkage and deduplication. In: Guillet, F., Hamilton, H. (eds.) Quality Measures in Data Mining Series. Studies in Computational Intelligence, pp. 127–151. Springer, Berlin (2007)CrossRefGoogle Scholar
  27. 27.
    Navarro, G.: A guided tour to approximate string matching. ACM Comput. Surv. 33(1), 31–88 (2001)CrossRefGoogle Scholar
  28. 28.
    Shannon, C.E.: A mathematical theory of communications. Bell Syst. Technol. 27, 379–423 (1948)MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Ukkonen, E.: Approximate string matching with q-grams and maximal matches. Theory Comput. Sci. 92, 191–211 (1992)MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Kukich, K.: Spelling correction for the telecommunications network for the deaf. Commun. ACM 35, 80–90 (1992)CrossRefGoogle Scholar
  31. 31.
    Gravano, L.; Ipeirotis, P.G.; Koudas, N.; Srivastava, D.: Text joins for data cleansing and integration in an RDBMS. In: Proceedings of the 19th IEEE International Conference on Data Engineering (ICDE) (2003)Google Scholar
  32. 32.
    Naumann, F.; Herschel, M.: An Introduction to Duplicate Detection. Morgan and Claypool Publishers, San Rafael (2010)MATHGoogle Scholar
  33. 33.
    Christen, P.; Goiser, K.: A comparison of personal name matching: techniques and practical issues. In: Proceeding of Data Mining Workshops, ICDM Workshops (2006)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Mayada A. Elziky
    • 1
  • Dina M. Ibrahim
    • 1
  • Amany M. Sarhan
    • 1
  1. 1.Department of Computers and Control Engineering, Faculty of EngineeringTanta UniversityTantaEgypt

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