Unsupervised blocking and probabilistic parallelisation for record matching of distributed big data

  • Chenxiao Dou
  • Yi Cui
  • Daniel Sun
  • Raymond Wong
  • Muhammad Atif
  • Guoqiang Li
  • Rajiv Ranjan
Article

Abstract

Record Matching refers to identifying pairs of records that relate to the same entities across different data sources. In many applications of data mining, record matching is usually associated to quadratic complexity. In practice, the number of non-matching record pairs always far exceeds the number of matching pairs, and this is called imbalance problem. Blocking is a technique of data reduction, which can filter unlikely matching pairs before record matching. However, for big data there is no fast and effective blocking algorithm yet. In this paper, we report on big data infrastructure to improve efficiency of blocking. Our approach runs blocking process independently and distributedly on the partitions of whole data. To improve efficiency, we adopt a probabilistic technique to balance the speed and the effect of the algorithm that we proposed for distributed blocking. Our experimental analysis endorses the superiority of our technique and shows its novel scalability.

Keywords

Big data Record matching Blocking Density Parallelisation 

References

  1. 1.
    Elmagarmid AK, Ipeirotis PG, Verykios VS (2007) Duplicate record detection: a survey. IEEE Trans Knowl Data Eng 19(1):1Google Scholar
  2. 2.
    Bilenko M, Kamath B, Mooney RJ (2006) Adaptive blocking: learning to scale up record linkage. In: ICDM’06 Sixth International Conference on Data Mining (IEEE, 2006), pp 87–96Google Scholar
  3. 3.
    Michelson M, Knoblock CA (2006) Learning blocking schemes for record linkage. In: Proceedings of the National Conference on Artificial Intelligence, vol 21. (Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2006), p 440Google Scholar
  4. 4.
    Whang SE, Menestrina D, Koutrika G, Theobald M, Garcia-Molina H (2009) Entity resolution with iterative blocking. In: SIGMOD Conference, pp 219–232Google Scholar
  5. 5.
    Dou C, Sun D, Wong R (2016) Unsupervised blocking of imbalanced datasets for record matching. In: International Conference on Web Information Systems Engineering. Springer, BerlinGoogle Scholar
  6. 6.
    Dou C, Sun D, Chen YC, Li G, Liu J (2016) Probabilistic parallelisation of blocking non-matched records for big data. In: 2016 IEEE International Conference on Big Data (Big Data), pp 3465–3473. doi:10.1109/BigData.2016.7841009
  7. 7.
    Chaudhuri S, Chen BC, Ganti V, Kaushik R (2007) Example-driven design of efficient record matching queries. In: Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB Endowment), pp 327–338Google Scholar
  8. 8.
    Bilenko M, Mooney RJ (2003) On evaluation and training-set construction for duplicate detection. In: Proceedings of the KDD-2003 Workshop on Data Cleaning, Record Linkage, and Object Consolidation, pp 7–12Google Scholar
  9. 9.
    Arasu A, Götz M, Kaushik R (2010) On active learning of record matching packages. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (ACM), pp 783–794Google Scholar
  10. 10.
    Sarawagi S, Bhamidipaty A (2002) Interactive deduplication using active learning. In: KDD. ACM, pp 269–278Google Scholar
  11. 11.
    Tong S, Koller D (2001) Support vector machine active learning with applications to text classification. J Mach Learn Res 2:45MATHGoogle Scholar
  12. 12.
    Tejada S, Knoblock CA, Minton S (2002) Learning domain-independent string transformation weights for high accuracy object identification. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp 350–359Google Scholar
  13. 13.
    Newcombe HB (1988) Handbook of record linkage: methods for health and statistical studies, administration, and business. Oxford University Press, OxfordGoogle Scholar
  14. 14.
    Wang R, Sun D, Li G, Atif M, Nepal S, LogProv (2016) Logging events as provenance of big data analytics pipelines with trustworthiness. In: 2016 IEEE International Conference on Big Data (Big Data), pp 1402–1411. doi:10.1109/BigData.2016.7840748
  15. 15.
    Wu D, Zhu L, Xu X, Sakr S, Sun D, Lu Q (2016) Building pipelines for heterogeneous execution environments for big data processing. IEEE Softw 33(2):60. doi:10.1109/MS.2016.35 CrossRefGoogle Scholar
  16. 16.
    Akbudak K, Aykanat C (2017) Exploiting locality in sparse matrix–matrix multiplication on many-core architectures. IEEE Trans Parallel Distrib Syst PP(99):1–1. doi:10.1109/TPDS.2017.2656893
  17. 17.
    Kunfang S, Lu H (2016) Efficient querying distributed big-XML data using MapReduce. Int J Grid High Perform Comput 8(3):70Google Scholar
  18. 18.
    Zeng Q, Zhao M, Liu P, Yadav P, Calo S, Lobo J (2015) Enforcement of autonomous authorizations in collaborative distributed query evaluation. IEEE Trans Knowl Data Eng 27(4):979CrossRefGoogle Scholar
  19. 19.
    Slagter K, Hsu CH, Chung YC (2015) An adaptive and memory efficient sampling mechanism for partitioning in MapReduce. Int J Parallel Program 43(3):489CrossRefGoogle Scholar
  20. 20.
    Christen P, Churches T, Hegland M (2004) Febrl—a parallel open source data linkage system. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, pp 638–647Google Scholar
  21. 21.
    Kim Hs, Lee D (2007) Parallel linkage. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management. ACM, pp 283–292Google Scholar
  22. 22.
    Efthymiou V, Stefanidis K, Christophides V (2015) Big data entity resolution: from highly to somehow similar entity descriptions in the Web. In: 2015 IEEE International Conference on Big Data (Big Data). IEEE, pp 401–410Google Scholar
  23. 23.
    Christen P (2012) A survey of indexing techniques for scalable record linkage and deduplication. IEEE Trans Knowl Data Eng 24(9):1537CrossRefGoogle Scholar
  24. 24.
    Kolb L, Thor A, Rahm E (2012) Dedoop: efficient deduplication with Hadoop. Proc VLDB Endow 5(12):1878CrossRefGoogle Scholar
  25. 25.
    Hernández MA, Stolfo SJ (1995) The merge/purge problem for large databases. In: ACM Sigmod Record, vol 24. ACM, pp 127–138Google Scholar
  26. 26.
    Kolb L, Thor A, Rahm E (2012) Multi-pass sorted neighborhood blocking with MapReduce. Comput Sci Res Dev 27(1):45CrossRefGoogle Scholar
  27. 27.
    Efthymiou V, Papadakis G, Papastefanatos G, Stefanidis K, Palpanas T (2015) Parallel meta-blocking: realizing scalable entity resolution over large, heterogeneous data. In: 2015 IEEE International Conference on Big Data (Big Data). IEEE, pp 411–420Google Scholar
  28. 28.
    Papadakis G, Koutrika G, Palpanas T, Nejdl W (2014) Meta-blocking: taking entity resolutionto the next level. IEEE Trans Knowl Data Eng 26(8):1946CrossRefGoogle Scholar
  29. 29.
    Wang L, Tao J, Ranjan R, Marten H, Streit A, Chen J, Chen D (2013) G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Gener Comput Syst 29(3):739CrossRefGoogle Scholar
  30. 30.
    Jayalath C, Stephen J, Eugster P (2014) From the cloud to the atmosphere: running mapreduce across data centers. IEEE Trans Comput 63(1):74MathSciNetCrossRefGoogle Scholar
  31. 31.
    Luo Y, Plale B (2012) Hierarchical mapreduce programming model and scheduling algorithms. In: Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012). IEEE Computer Society, pp 769–774Google Scholar
  32. 32.
    Shabeera T, Madhu Kumar S (2015) Optimising virtual machine allocation in MapReduce cloud for improved data locality. Int J Big Data Intell 2(1):2CrossRefGoogle Scholar
  33. 33.
    Hsu CH, Slagter KD, Chung YC (2015) Locality and loading aware virtual machine mapping techniques for optimizing communications in mapreduce applications. Future Gener Comput Syst 53:43CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Chenxiao Dou
    • 1
  • Yi Cui
    • 2
  • Daniel Sun
    • 1
    • 3
  • Raymond Wong
    • 1
  • Muhammad Atif
    • 4
  • Guoqiang Li
    • 2
  • Rajiv Ranjan
    • 5
  1. 1.University of New South WalesSydneyAustralia
  2. 2.School of SoftwareShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Data61, CSIROCanberraAustralia
  4. 4.National Computational InfrastructureCanberraAustralia
  5. 5.Newcastle UniversityNewcastle upon TyneUK

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