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Flexible and Efficient Distributed Resolution of Large Entities

  • Conference paper
Foundations of Information and Knowledge Systems (FoIKS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7153))

Abstract

Entity resolution (ER) is a computationally hard problem of data integration scenarios, where database records have to be grouped according to the real-world entities they belong to. In practice these entities may consist of only a few records from different data sources with typos or historical data. In other cases they may contain significantly more records, especially when we search for entities on a higher level of a concept hierarchy than records.

In this paper we give theoretical foundation of a variety of practically important match functions. We show that under these formulations, ER with large entities can be solved efficiently with algorithms based on MapReduce, a distributed computing paradigm. Our algorithm can efficiently incorporate probabilistic and similarity-based record match, enabling flexible match function definition. We demonstrate the usability of our model and algorithm in a real-world insurance ER scenario, where we identify household groups of client records.

This work was supported by the EU FP7 SEC project SCIIMS (Ref. 218223).

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References

  1. Benjelloun, O., Garcia-Molina, H., Gong, H., Kawai, H., Larson, T.E., Menestrina, D., Thavisomboon, S.: D-Swoosh: A family of algorithms for generic, distributed entity resolution. In: Proc. 27th Int. Conf. on Distributed Computing Systems (2007)

    Google Scholar 

  2. Benjelloun, O., Garcia-Molina, H., Menestrina, D., Su, Q., Whang, S.E., Widom, J.: Swoosh: a generic approach to entity resolution. VLDB J. 18(1), 255–276 (2009)

    Article  Google Scholar 

  3. Bhattacharya, I., Getoor, L.: A Latent dirichlet model for unsupervised entity resolution. In: SIAM International Conference on Data Mining, pp. 47–58 (2006)

    Google Scholar 

  4. Bhattacharya, I., Getoor, L.: Collective entity resolution in relational data. ACM Trans. Knowl. Discov. Data 1(1), 5 (2007)

    Article  Google Scholar 

  5. Bhattacharya, I., Getoor, L., Licamele, L.: Query-time entity resolution. In: Proc. 12th ACM SIGKDD, pp. 529–534 (2006)

    Google Scholar 

  6. Bhattacharya, I., Godbole, S., Joshi, S.: Structured entity identification and document categorization: two tasks with one joint model. In: KDD 2008: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 25–33. ACM, New York (2008)

    Google Scholar 

  7. Bilenko, M., Mooney, R.: Adaptive duplicate detection using learnable string similarity measures. In: Proc. 9th ACM SIGKDD, pp. 39–48 (2003)

    Google Scholar 

  8. Boley, M., Horváth, T., Poigné, A., Wrobel, S.: Efficient Closed Pattern Mining in Strongly Accessible Set Systems (Extended Abstract). In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 382–389. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Chaudhuri, S., Sarma, A.D., Ganti, V., Kaushik, R.: Leveraging aggregate constraints for deduplication. In: SIGMOD 2007, pp. 437–448. ACM (2007)

    Google Scholar 

  10. Chen, S., Borthwick, A., Carvalho, V.R.: The case for cost-sensitive and easy-to-interpret models in industrial record linkage. In: 9th International Workshop on Quality in Databases (2011)

    Google Scholar 

  11. Christen, P.: Automatic record linkage using seeded nearest neighbour and support vector machine classification. In: KDD 2008, pp. 151–159. ACM (2008)

    Google Scholar 

  12. Christen, P.: A survey of indexing techniques for scalable record linkage and deduplication. In: IEEE TKDE preprint (2011)

    Google Scholar 

  13. Christen, P., Churches, T., Hegland, M.: Febrl – A Parallel Open Source Data Linkage System. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 638–647. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  14. Christen, P., Gayler, R., Hawking, D.: Similarity-aware indexing for real-time entity resolution. In: CIKM 2009, pp. 1565–1568. ACM (2009)

    Google Scholar 

  15. Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press (2001)

    Google Scholar 

  16. Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  17. Elmagarmid, A., Ipeirotis, P., Verykios, V.: Duplicate Record Detection: A Survey. IEEE TKDE, 1–16 (2007)

    Google Scholar 

  18. Fellegi, I., Sunter, A.: A theory for record linkage. Journal of the American Statistical Association 64(328), 1183–1210 (1969)

    Article  MATH  Google Scholar 

  19. Getoor, L., Diehl, C.: Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 3–12 (2005)

    Article  Google Scholar 

  20. Gravano, L., Ipeirotis, P., Jagadish, H., Koudas, N., Muthukrishnan, S., Srivastava, D.: Approximate string joins in a database (almost) for free. In: VLDB, pp. 491–500 (2001)

    Google Scholar 

  21. Guo, S., Dong, X.L., Srivastava, D., Zajac, R.: Record linkage with uniqueness constraints and erroneous values. Proc. VLDB Endow. 3, 417–428 (2010)

    Article  Google Scholar 

  22. Hall, R., Sutton, C., McCallum, A.: Unsupervised deduplication using cross-field dependencies. In: KDD 2008, pp. 310–317. ACM (2008)

    Google Scholar 

  23. Han, H., Xu, W., Zha, H., Giles, C.: A hierarchical naive Bayes mixture model for name disambiguation in author citations. In: Proc. 2005 ACM Symposium on Applied Computing, pp. 1065–1069 (2005)

    Google Scholar 

  24. Hernández, M., Stolfo, S.: Real-world data is dirty: Data cleansing and the merge/purge problem. Data Mining and Knowledge Discovery 2(1), 9–37 (1998)

    Article  Google Scholar 

  25. Kang, U., Tsourakakis, C., Faloutsos, C.: Pegasus: A peta-scale graph mining system implementation and observations. In: ICDM, pp. 229–238. IEEE (2009)

    Google Scholar 

  26. Kim, H.-S., Lee, D.: Parallel linkage. In: CIKM 2007. ACM (2007)

    Google Scholar 

  27. Kirsten, T., Kolb, L., Hartung, M., Gross, A., Köpcke, H., Rahm, E.: Data partitioning for parallel entity matching. Computing Research Repository (2010)

    Google Scholar 

  28. Köpcke, H., Rahm, E.: Training selection for tuning entity matching. In: QDB/MUD, pp. 3–12 (2008)

    Google Scholar 

  29. Köpcke, H., Rahm, E.: Frameworks for entity matching: A comparison. Data Knowl. Eng. 69, 197–210 (2010)

    Article  Google Scholar 

  30. Köpcke, H., Thor, A., Rahm, E.: Evaluation of entity resolution approaches on real-world match problems. Proc. VLDB Endow. 3, 484–493 (2010)

    Article  Google Scholar 

  31. McCarthy, J., Lehnert, W.: Using decision trees for coreference resolution. In: Proc. 14th Int. Conf. on Artificial Intelligence, pp. 1050–1055 (1995)

    Google Scholar 

  32. Menestrina, D., Benjelloun, O., Garcia-Molina, H.: Generic entity resolution with data confidences. In: CleanDB Workshop, pp. 25–32 (2006)

    Google Scholar 

  33. Menestrina, D., Whang, S.E., Garcia-Molina, H.: Evaluating entity resolution results. Proc. VLDB Endow. 3, 208–219 (2010)

    Article  Google Scholar 

  34. Sarawagi, S., Bhamidipaty, A.: Interactive deduplication using active learning. In: SIGKDD, pp. 269–278 (2002)

    Google Scholar 

  35. Sidló, C.I.: Generic Entity Resolution in Relational Databases. In: Grundspenkis, J., Morzy, T., Vossen, G. (eds.) ADBIS 2009. LNCS, vol. 5739, pp. 59–73. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  36. Sidló, C.I.: Entity resolution with heavy indexing. In: Proc. ADBIS, CEUR Workshop Proceedings (2011)

    Google Scholar 

  37. Sidló, C.I., Garzó, A., Molnár, A., Benczúr, A.A.: Infrastructures and bounds for distributed entity resolution. In: 9th International Workshop on Quality in Databases (2011)

    Google Scholar 

  38. Talburt, J.R.: Entity Resolution and Information Quality, 1st edn. Morgan Kaufmann (2010)

    Google Scholar 

  39. Weis, M., Naumann, F., Jehle, U., Lufter, J., Schuster, H.: Industry-scale duplicate detection. Proc. of the VLDB Endow. 1(2), 1253–1264 (2008)

    Article  Google Scholar 

  40. Whang, S.E., Garcia-Molina, H.: Entity resolution with evolving rules. Proc. VLDB Endow. 3, 1326–1337 (2010)

    Article  Google Scholar 

  41. Whang, S.E., Menestrina, D., Koutrika, G., Theobald, M., Garcia-Molina, H.: Entity resolution with iterative blocking. In: Proc. 35th Int. Conf. on Management of Data, pp. 219–232. ACM (2009)

    Google Scholar 

  42. White, T.: Hadoop: The Definitive Guide. Yahoo Press (2010)

    Google Scholar 

  43. Wick, M.L., Rohanimanesh, K., Schultz, K., McCallum, A.: A unified approach for schema matching, coreference and canonicalization. In: KDD 2008, pp. 722–730. ACM (2008)

    Google Scholar 

  44. Yakout, M., Elmagarmid, A.K., Elmeleegy, H., Ouzzani, M., Qi, A.: Behavior based record linkage. Proc. VLDB Endow. 3, 439–448 (2010)

    Article  Google Scholar 

  45. Zhang, Z., Hadjieleftheriou, M., Ooi, B.C., Srivastava, D.: Bed-tree: an all-purpose index structure for string similarity search based on edit distance. In: SIGMOD, pp. 915–926. ACM (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Data Mining and Web Search Group, Informatics Laboratory Institute for Computer Science and Control, Hungarian Academy of Sciences, Hungary

    András J. Molnár, András A. Benczúr & Csaba István Sidló

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  1. András J. Molnár
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  2. András A. Benczúr
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  3. Csaba István Sidló
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Editors and Affiliations

  1. Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, OX1 3QD, Oxford, UK

    Thomas Lukasiewicz

  2. Alfréd Rényi Institute, Hungarian Academy of Sciences, Reáltanoda utca 13-15, 1053, Budapest, Hungary

    Attila Sali

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Molnár, A.J., Benczúr, A.A., Sidló, C.I. (2012). Flexible and Efficient Distributed Resolution of Large Entities. In: Lukasiewicz, T., Sali, A. (eds) Foundations of Information and Knowledge Systems. FoIKS 2012. Lecture Notes in Computer Science, vol 7153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28472-4_14

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  • DOI: https://doi.org/10.1007/978-3-642-28472-4_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28471-7

  • Online ISBN: 978-3-642-28472-4

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