Advertisement

Exploiting Redundancy, Recurrency and Parallelism: How to Link Millions of Addresses with Ten Lines of Code in Ten Minutes

  • Yuhang Zhang
  • Tania Churchill
  • Kee Siong Ng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 845)

Abstract

Accurate and efficient record linkage is an open challenge of particular relevance to Australian Government Agencies, who recognise that so-called wicked social problems are best tackled by forming partnerships founded on large-scale data fusion. Names and addresses are the most common attributes on which data from different government agencies can be linked. In this paper, we focus on the problem of address linking. Linkage is particularly problematic when the data has significant quality issues. The most common approach for dealing with quality issues is to standardise raw data prior to linking. If a mistake is made in standardisation, however, it is usually impossible to recover from it to perform linkage correctly. This paper proposes a novel algorithm for address linking that is particularly practical for linking large disparate sets of addresses, being highly scalable, robust to data quality issues and simple to implement. It obviates the need for labour intensive and problematic address standardisation. Empirical results show that approximately \(91\%\) of the generated links created by matching two large address datasets from two government agencies, were correct. Finally, we demonstrate that the linking can be performed in under 10 min, with 10 lines of code.

Keywords

Record linkage Address linking 

References

  1. 1.
    Arasu, A., Ganti, V., Kaushik, R.: Efficient exact set-similarity joins. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 918–929. VLDB Endowment (2006)Google Scholar
  2. 2.
    Cands, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207–1223 (2006)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chang, L., Wang, Z., Ma, T., Jian, L., Ma, L., Goldshuv, A., Lonergan, L., Cohen, J., Welton, C., Sherry, G., Bhandarkar, M.: HAWQ: a massively parallel processing SQL engine in Hadoop. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 1223–1234. ACM, New York (2014)Google Scholar
  4. 4.
    Christen, P.: Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-31164-2CrossRefGoogle Scholar
  5. 5.
    Christen, P., Belacic, D.: Automated probabilistic address standardisation and verification. In: Australasian Data Mining Conference (AusDM05) (2005)Google Scholar
  6. 6.
    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).  https://doi.org/10.1007/978-3-540-24775-3_75CrossRefGoogle Scholar
  7. 7.
    Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A comparison of string metrics for matching names and records. In: SIGKDD (2003)Google Scholar
  8. 8.
    Gormley, C., Tong, Z.: Elasticsearch: The Definitive Guide. O’Reilly Media, Sebastopol (2015)Google Scholar
  9. 9.
    Guo, H., Zhu, H., Guo, Z., Zhang, X., Su, Z.: Address standardization with latent semantic association. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 1155–1164. ACM, New York (2009)Google Scholar
  10. 10.
    Halevy, A., Norvig, P., Pereira, F.: The unreasonable effectiveness of data. IEEE Intell. Syst. 24, 8–12 (2009)CrossRefGoogle Scholar
  11. 11.
    Hellerstein, J.M., Ré, C., Schoppmann, F., Wang, D.Z., Fratkin, E., Gorajek, A., Ng, K.S., Welton, C., Feng, X., Li, K., Kumar, A.: The MADlib analytics library or MAD skills, the SQL. PVLDB 5(12), 1700–1711 (2012)Google Scholar
  12. 12.
    Kornacker, M., Behm, A., Bittorf, V., Bobrovytsky, T., Ching, C., Choi, A., Erickson, J., Grund, M., Hecht, D., Jacobs, M., Joshi, I., Kuff, L., Kumar, D., Leblang, A., Li, N., Pandis, I., Robinson, H., Rorke, D., Rus, S., Russell, J., Tsirogiannis, D., Wanderman-Milne, S., Yoder, M.: Impala: a modern, open-source SQL engine for Hadoop. In: CIDR (2015)Google Scholar
  13. 13.
    Monge, A., Elkan, C.: An efficient domain-independent algorithm for detecting approximately duplicate database records. In: DMKD 1997 (1997)Google Scholar
  14. 14.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud 2010, p. 10. USENIX Association, Berkeley (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Australian Transaction Reports and Analysis CentreCanberraAustralia

Personalised recommendations