Skip to main content
Log in

String similarity join with different similarity thresholds based on novel indexing techniques

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

String similarity join is an essential operation of many applications that need to find all similar string pairs from two given collections. A quantitative way to determine whether two strings are similar is to compute their similarity based on a certain similarity function. The string pairs with similarity above a certain threshold are regarded as results. The current approach to solving the similarity join problem is to use a unique threshold value. There are, however, several scenarios that require the support of multiple thresholds, for instance, when the dataset includes strings of various lengths. In this scenario, longer string pairs typically tolerate much more typos than shorter ones. Therefore, we proposed a solution for string similarity joins that supports different similarity thresholds in a single operator. In order to support different thresholds, we devised two novel indexing techniques: partition based indexing and similarity aware indexing. To utilize the new indices and improve the join performance, we proposed new filtering methods and index probing techniques. To the best of our knowledge, this is the first work that addresses this problem. Experimental results on real-world datasets show that our solution performs efficiently while providing a more flexible threshold specification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Monge A, Elkan C. The field matching problem: algorithms and applications. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1996, 267–270

    Google Scholar 

  2. Zhang Z J, Hadjieleftheriou M, Ooi B, Srivastava D. Bed-tree: an allpurpose index structure for string similarity search based on edit distance. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2010, 915–926

    Google Scholar 

  3. Lu W, Du X Y, Hadjieleftheriou M, Ooi B C. Efficiently supporting edit distance based string similarity search using b+-trees. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(12): 2983–2996

    Article  Google Scholar 

  4. Wang J N, Feng J H, Li G L. Trie-join: efficient trie-based string similarity joins with edit-distance constraints. Proceedings of the VLDB Endowment, 2010, 3(1–2): 1219–1230

    Article  Google Scholar 

  5. Sarawagi S, Kirpal A. Efficient set joins on similarity predicates. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2004, 743–754

    Google Scholar 

  6. Chaudhuri S, Ganti V, Kaushik R. A primitive operator for similarity joins in data cleaning. In: Proceedings of the 22nd IEEE International Conference on Data Engineering. 2006, 61–72

    Google Scholar 

  7. Bayardo R, Ma Y, Srikant R. Scaling up all pairs similarity search. In: Proceedings of the 16th ACM International Conference on World Wide Web. 2007, 131–140

    Chapter  Google Scholar 

  8. Xiao C, Wang W, Lin X M, Yu J. Efficient similarity joins for near duplicate detection. In: Proceedings of ACM International Conference on World Wide Web. 2008, 563–574

    Google Scholar 

  9. Hernández M, Stolfo S. The merge/purge problem for large databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 1995, 127–138

    Google Scholar 

  10. Winkler W E. The state of record linkage and current research problems. Technical Report, Statistical Research Division, U.S. Census Bureau. 1999

    Google Scholar 

  11. Sivic J, Zisserman A. Video google: a text retrieval approach to object matching in videos. 2003, 1470–1477

    Google Scholar 

  12. Dong X, Halevy A, Madhavan J. Reference reconciliation in complex information spaces. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2005, 85–96

    Google Scholar 

  13. Sarawagi S, Bhamidipaty A. Interactive deduplication using active learning. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2002, 269–278

    Google Scholar 

  14. Arasu A, Ré C, Suciu D. Large-scale deduplication with constraints using dedupalog. In: Proceedings of the 25th IEEE International Conference on Data Engineering. 2009, 952–963

    Google Scholar 

  15. Gravano L, Ipeirotis P G, Jagadish H V, Koudas N, Muthukrishnan S, Srivastava D. Approximate string joins in a database (almost) for free. In: Proceedings of the VLDB Endowment. 2001, 491–500

    Google Scholar 

  16. Elmagarmid A K, Ipeirotis P G, Verykios V S. Duplicate record detection: a survey. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(1): 1–16

    Article  Google Scholar 

  17. Naumann F, Herschel M. An Introduction to duplicate detection. Synthesis Lectures on Data Management, 2010, 2(1): 1–87

    Article  MATH  Google Scholar 

  18. Jiang Y, Li G L, Feng J H, Li W S. String similarity joins: an experimental evaluation. Proceedings of the VLDB Endowment, 2014, 7(8): 625–636

    Article  Google Scholar 

  19. Chaudhuri S, Kaushik R. Extending autocompletion to tolerate errors. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2009, 707–718

    Chapter  Google Scholar 

  20. Deng D, Li G L, Feng J H. A pivotal prefix based filtering algorithm for string similarity search. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2014, 673–684

    Google Scholar 

  21. Wang J N, Li G L, Feng J H. Can we beat the prefix filtering?: an adaptive framework for similarity join and search. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2012, 85–96

    Google Scholar 

  22. Rong C T, LuW, Wang X L, Du X Y, Chen Y G, Tung A K H. Efficient and scalable processing of string similarity join. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(10): 2217–2230

    Article  Google Scholar 

  23. Lu J H, Lin C B, Wang W, Li C, Wang H Y. String similarity measures and joins with synonyms. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2013, 373–384

    Google Scholar 

  24. Li G L, He J, Deng D, Li J. Efficient similarity join and search on multi-attribute data. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2015, 1137–1151

    Google Scholar 

  25. Salton G, McGill M J. Introduction to Modern Information Retrieval. New York: McGraw-Hill, Inc., 1986

    MATH  Google Scholar 

  26. Witten I H, Moffat A, Bell T C. Managing Gigabytes: Compressing and Indexing Documents and Images. 2nd ed. San Francisco, CA: Morgan Kaufmann, 1999

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by China Scholarship Council and the National Natural Science Foundation of China (Grant Nos. 61402329 and 51378350).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chuitian Rong.

Additional information

Chuitian Rong is an associate professor at Tianjin Polytechnic University, China. He received his PhD degree from Renmin University of China, China in 2013. His research interests are database system, information retrieval, and big data analysis.

Yasin N. Silva is an associate professor of applied computing in the School of Mathematical & Natural Sciences at Arizona StateUniversity, USA.He received his PhD (2010) and MS (2006) in computer science from Purdue University, USA and his BS (2000) in computer engineering from the Pontificia Universidad Catolica, Peru. Yasin’s research areas deal with data management systems and privacy preservation in general.More specifically, he has been working on the areas of query processing and optimization, privacy assurance in database systems, big data management systems, scientific database systems, and the integration of new data processing technologies into the computing curricula.

Chunqing Li is a professor at Tianjin Polytechnic University, China. His research interests are database system and applications, big data analysis, and network management and applications.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rong, C., Silva, Y.N. & Li, C. String similarity join with different similarity thresholds based on novel indexing techniques. Front. Comput. Sci. 11, 307–319 (2017). https://doi.org/10.1007/s11704-016-5231-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-016-5231-1

Keywords

Navigation