MapReduce-based entity matching with multiple blocking functions

Abstract

Entity matching that aims at finding some records belonging to the same real-world objects has been studied for decades. In order to avoid verifying every pair of records in a massive data set, a common method, known as the blockingbased method, tends to select a small proportion of record pairs for verification with a far lower cost thanO(n²), where n is the size of the data set. Furthermore, executing multiple blocking functions independently is critical since much more matching records can be found in this way, so that the quality of the query result can be improved significantly.

It is popular to use the MapReduce (MR) framework to improve the performance and the scalability of some complicated queries by running a lot of map (/reduce) tasks in parallel. However, entity matching upon the MapReduce framework is non-trivial due to two inevitable challenges: load balancing and pair deduplication. In this paper, we propose a novel solution, called MrEm, to handle these challenges with the support of multiple blocking functions. Although the existing work can deal with load balancing and pair deduplication respectively, it still cannot deal with both challenges at the same time. Theoretical analysis and experimental results upon real and synthetic data sets illustrate the high effectiveness and efficiency of our proposed solutions.

Keywords

entity matching / MapReduce / load balancing / pair deduplication

References

[1] BenjellounO, Garcia-Molina H, MenestrinaD , SuQ, WhangS E, WidomJ. Swoosh: a generic approach to entity resolution. The VLDB Journal—The International Journal on Very Large Data Bases, 2009, 18(1): 255–276

[2] BilenkoM, MooneyR J. Adadptive duplicate detection using learnable string similarity measures. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2003, 39–48

[3] GuoS T, DongX L, SrivastavaD , ZajacR. Record linkage with uniqueness constraints and erroneous values. Proceedings of the VLDB Endowment, 2010, 3(1–2): 417–428

[4] LiP, DongX L, MaurinoA, Srivastava D. Linkingtemporal records. Proceedings of the VLDB Endowment, 2011, 4(11): 956–967

[5] RastogiV, DalviN, GarofalakisM . Large-scale collective entity matching. Proceedings of the VLDB Endowment, 2011, 4(4): 208–218

[6] BilenkoM, KamathB, MooneyR J. Adaptive blocking: learning to scale up record linkage. In: Proceedings of the 6th IEEE International Conference on Data Mining. 2006, 87–96

[7] ChristenP. A survey of indexing techniques for scalable record linkage and deduplication. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(9): 1537–1555

[8] De VriesT, KeH, ChawlaS, Christen P. Robust record linkage blocking using suffix arrays and bloom filters. ACM Transactions on Knowledge Discovery from Data, 2011, 5(2): 9

[9] MichelsonM, Knoblock C A. Learning blocking schemes for record linkage. In: Proceedings of the National Conference on Artificial Intelligence. 2006, 440–445

[10] FellegiI P, SunterA B. A theory for record linkage. Journal of the American Statistical Association, 1969, 64(328): 1183–1210

[11] HernándezM A, Stolfo S J. The merge/purge problem for large databases. ACM SIGMOD Record, 1995, 24(2): 127–138

[12] GionisA, IndykP, MotwaniR. Similarity search in high dimensions via hashing. The VLDB Journal — The International Journal on Very Large Data Bases, 1999, 99(6): 518–529

[13] IndykP, Motwani R. Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the 30th Annual ACM Symposium on Theory of Computing. 1998, 604–613

[14] KolbL, ThorA, RahmE. Multi-pass sorted neighborhood blocking with MapReduce. Computer Science-Research and Development, 2012, 27(1): 45–63

[15] WhangS E, Menestrina D, KoutrikaG , TheobaldM, Garcia-Molina H. Entity resolution with iterative blocking. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data. 2009, 219–232

[16] KolbL, ThorA, RahmE. Load balancing for MapReduce-based entity resolution. In: Proceedings of the 28th IEEE International Conference on Data Engineering. 2012, 618–629

[17] KöpckeH, ThorA, RahmE. Evaluation of entity resolution approaches on real-world match problems. Proceedings of the VLDB Endowment, 2010, 3(1–2): 484–493

[18] KolbL, ThorA, RahmE. Don’t match twice:redundancy-free similarity computation with MapReduce. In: Proceedings of the 2nd Workshop on Data Analytics in the Cloud. 2013, 1–5

[19] KolbL, RahmE. Parallel entity resolution with dedoop. Datenbank- Spektrum, 2013, 13(1): 23–32

[20] DeanJ, Ghemawat S. MapReduce: simplified data processing on large clusters. Communications of the ACM, 2008, 51(1): 107–113

[21] WhiteT. Hadoop: The Definitive Guide. 3rd ed. O’Reilly Media, Inc., 2012

[22] MitzenmacherM. Compressed bloom filters. IEEE/ACM Transactions on Networking, 2002, 10(5): 604–612

[23] VernicaR, CareyM J, LiC. Efficient parallel set-similarity joins using MapReduce. In: Proceedings of the 2010 ACMSIGMOD International Conference on Management of Data. 2010, 495–506

[24] BaxterR, Christen P, ChurchesT . A comparison of fast blocking methods for record linkage. ACM SIGKDD, 2003, 3: 25–27

[25] CohenW W, Richman J. Learning to match and cluster large highdimensional data sets for data integration. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2002, 475–480

[26] JinL, LiC, MehrotraS. Efficient record linkage in large data sets. In: Proceedings of the 8th International Conference on Database Systems for Advanced Applications. 2003, 137–146

[27] HeY B, TanH Y, LuoW M, Feng S Z, FanJ P . MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data. Frontiers of Computer Science, 2014, 8(1): 83–99

[28] Das SarmaA, HeY Y, ChaudhuriS. Clusterjoin: a similarity joins framework using map-reduce. Proceedings of the VLDB Endowment, 2014, 7(12): 1059–1070

[29] DengD, LiG L, HaoS, Wang J N, FengJ H . Massjoin: a MapReducebased method for scalable string similarity joins. In: proceedings of the 30th IEEE International Conference on Data Engineering. 2014, 340–351

[30] KimY, ShimK. Parallel top-k similarity join algorithms using MapReduce. In: Proceedings of the 28th IEEE International Conference on Data Engineering. 2012, 510–521

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2016 Higher Education Press and Springer-Verlag Berlin Heidelberg