Edjoin: An efficient algorithm for similarity joins with edit distance constraints

Proceedings of the VLDB Endowment

Volumn 1, Issue 1, 2008, Pages 933-944

  Xiao, Chuan ^a   Wang, Wei ^a   Lin, Xuemin ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION;

APPLICATION AREA; COMPUTATION TIME; FILTERING METHOD; FUNDAMENTAL OPERATIONS; PARAMETER SETTING; RESEARCH COMMUNITIES; SIMILARITY JOIN; SUBSTANTIAL REDUCTION;

DATA INTEGRATION;

EID: 70849105253     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/1453856.1453957     Document Type: Article

References (35)

1. A. Andoni, M. Deza, A. Gupta, P. Indyk, and S. Raskhodnikova. Lower bounds for embedding edit distance into normed spaces. In SODA, pages 523-526, 2003.
2. A. Arasu, V. Ganti, and R. Kaushik. Efficient exact set-similarity joins. In VLDB, 2006.
3. R. J. Bayardo, Y. Ma, and R. Srikant. Scaling up all pairs similarity search. In WWW, 2007.
4. M. Bilenko, R. J. Mooney, W. W. Cohen, P. Ravikumar, and S. E. Fienberg. Adaptive name matching in information integration. IEEE Intelligent Sys., 18(5):16-23, 2003.
5. C. Böhm, B. Braunmüller, F. Krebs, and H.-P. Kriegel. Epsilon grid order: An algorithm for the similarity join on massive high-dimensional data. In SIGMOD, pages 379-388, 2001.
6. A. Z. Broder, S. C. Glassman, M. S. Manasse, and G. Zweig. Syntactic clustering of the web. Computer Networks, 29(8-13):1157-1166, 1997.
7. S. Burkhardt and J. Kärkkäinen. One-gapped q-gram filters for Levenshtein distance. In CPM, pages 225-234, 2002.
8. A. Chandel, O. Hassanzadeh, N. Koudas, M. Sadoghi, and D. Srivastava. Benchmarking declarative approximate selection predicates. In SIGMOD, pages 353-364, 2007.
9. M. Charikar. Similarity estimation techniques from rounding algorithms. In STOC, 2002.
10. S. Chaudhuri, B.-C. Chen, V. Ganti, and R. Kaushik. Example-driven design of efficient record matching queries. In VLDB, pages 327-338, 2007.
11. S. Chaudhuri, V. Ganti, and R. Kaushik. Data debugger: An operator-centric approach for data quality solutions. IEEE Data Eng. Bull., 29(2):60-66, 2006.
12. S. Chaudhuri, V. Ganti, and R. Kaushik. A primitive operator for similarity joins in data cleaning. In ICDE, 2006.
13. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In VLDB, 1999.
14. L. Gravano, P. G. Ipeirotis, H. V. Jagadish, N. Koudas, S. Muthukrishnan, and D. Srivastava. Approximate string joins in a database (almost) for free. In VLDB, 2001.
15. L. Gravano, P. G. Ipeirotis, H. V. Jagadish, N. Koudas, S. Muthukrishnan, and D. Srivastava. Approximate string joins in a database (almost) for free (erratum). Technical Report CUCS-011-03, Columbia University, 2003.
16. D. Gusfield. Algorithms on Strings, Trees, and Sequences. Computer Science and Computational Biology. Cambridge University Press, 1997.
17. S. Helmer and G. Moerkotte. Evaluation of main memory join algorithms for joins with set comparison join predicates. In VLDB, pages 386-395, 1997.
18. M. R. Henzinger. Finding near-duplicate web pages: a large-scale evaluation of algorithms. In SIGIR, 2006.
19. M. A. Hernández and S. J. Stolfo. Real-world data is dirty: Data cleansing and the merge/purge problem. Data Mining and Knowledge Discovery, 2(1):9-37, 1998.
20. H. Lee, R. T. Ng, and K. Shim. Extending q-grams to estimate selectivity of string matching with low edit distance. In VLDB, pages 195-206, 2007.
21. C. Li, B. Wang, and X. Yang. VGRAM: Improving performance of approximate queries on string collections using variable-length grams. In VLDB, 2007.
22. N. Mamoulis. Efficient processing of joins on set-valued attributes. In SIGMOD, pages 157-168, 2003.
23. W. J. Masek and M. Paterson. A faster algorithm computing string edit distances. J. Comput. Syst. Sci., 20(1):18-31, 1980.
24. S. Melnik and H. Garcia-Molina. Adaptive algorithms for set containment joins. ACM Trans. Database Syst., 28:56-99, 2003.
25. G. Myers. A fast bit-vector algorithm for approximate string matching based on dynamic programming. J. ACM, 46(3):395-415, 1999.
26. G. Navarro. A guided tour to approximate string matching. ACM Comput. Surv., 33(1):31-88, 2001.
27. M. V. Ramakrishna and J. Zobel. Performance in practice of string hashing functions. In DASFAA, pages 215-224, 1997.
28. K. Ramasamy, J. M. Patel, J. F. Naughton, and R. Kaushik. Set containment joins: The good, the bad and the ugly. In VLDB, pages 351-362, 2000.
29. S. Sarawagi and A. Bhamidipaty. Interactive deduplication using active learning. In KDD, 2002.
30. S. Sarawagi and A. Kirpal. Efficient set joins on similarity predicates. In SIGMOD, 2004.
31. E. Ukkonen. On approximate string matching. In FCT, 1983.
32. R. A. Wagner and M. J. Fischer. The string-to-string correction problem. J. ACM, 21(1):168-173, 1974.
33. W. E. Winkler. The state of record linkage and current research problems. Technical report, U.S. Bureau of the Census, 1999.
34. C. Xiao, W. Wang, X. Lin, and J. X. Yu. Efficient similarity joins for near duplicate detection. In WWW, 2008.
35. J. Zobel and A. Moffat. Inverted files for text search engines. ACM Comput. Surv., 38(2), 2006.