Adaptive Temporal Entity Resolution on Dynamic Databases

  • Peter Christen
  • Ross W. Gayler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)


Entity resolution is the process of matching records that refer to the same entities from one or several databases in situations where the records to be matched do not include unique entity identifiers. Matching therefore has to rely upon partially identifying information, such as names and addresses. Traditionally, entity resolution has been applied in batch-mode and on static databases. However, increasingly organisations are challenged by the task of having a stream of query records that need to be matched to a database of known entities. As these query records are matched, they are inserted into the database as either representing a new entity, or as the latest embodiment of an existing entity. We investigate how temporal and dynamic aspects, such as time differences between query and database records and changes in database content, affect matching quality. We propose an approach that adaptively adjusts similarities between records depending upon the values of the records’ attributes and the time differences between records. We evaluate our approach on synthetic data and a large real US voter database, with results showing that our approach can outperform static matching approaches.


Data matching record linkage dynamic data real-time matching 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Winkler, W.E.: Methods for evaluating and creating data quality. Elsevier Information Systems 29(7), 531–550 (2004)CrossRefGoogle Scholar
  2. 2.
    Christen, P.: Data Matching. In: Data-Centric Systems and Appl., Springer (2012)Google Scholar
  3. 3.
    Elmagarmid, A., Ipeirotis, P., Verykios, V.: Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering 19(1), 1–16 (2007)CrossRefGoogle Scholar
  4. 4.
    Herzog, T., Scheuren, F., Winkler, W.: Data quality and record linkage techniques. Springer (2007)Google Scholar
  5. 5.
    Aggarwal, C.: Data Streams: Models and Algorithms. Database Management and Information Retrieval, vol. 31. Springer (2007)Google Scholar
  6. 6.
    Anderson, K., Durbin, E., Salinger, M.: Identity theft. Journal of Economic Perspectives 22(2), 171–192 (2008)CrossRefGoogle Scholar
  7. 7.
    Ioannou, E., Nejdl, W., Niederée, C., Velegrakis, Y.: On-the-fly entity-aware query processing in the presence of linkage. VLDB Endowment 3(1) (2010)Google Scholar
  8. 8.
    Li, P., Dong, X., Maurino, A., Srivastava, D.: Linking temporal records. Proceedings of the VLDB Endowment 4(11) (2011)Google Scholar
  9. 9.
    Li, P., Tziviskou, C., Wang, H., Dong, X., Liu, X., Maurino, A., Srivastava, D.: Chronos: Facilitating history discovery by linking temporal records. VLDB Endowment 5(12) (2012)Google Scholar
  10. 10.
    Whang, S., Garcia-Molina, H.: Entity resolution with evolving rules. VLDB Endowment 3(1-2), 1326–1337 (2010)Google Scholar
  11. 11.
    Yakout, M., Elmagarmid, A., Elmeleegy, H., Ouzzani, M., Qi, A.: Behavior based record linkage. VLDB Endowment 3(1-2), 439–448 (2010)Google Scholar
  12. 12.
    Christen, P., Gayler, R.: Towards scalable real-time entity resolution using a similarity-aware inverted index approach. In: AusDM 2008, Glenelg, Australia (2008)Google Scholar
  13. 13.
    Christen, P., Gayler, R., Hawking, D.: Similarity-aware indexing for real-time entity resolution. In: ACM CIKM 2009, Hong Kong, pp. 1565–1568 (2009)Google Scholar
  14. 14.
    Pal, A., Rastogi, V., Machanavajjhala, A., Bohannon, P.: Information integration over time in unreliable and uncertain environments. In: WWW, Lyon (2012)Google Scholar
  15. 15.
    Laxman, S., Sastry, P.: A survey of temporal data mining. Sadhana 31(2) (2006)Google Scholar
  16. 16.
    Christen, P., Pudjijono, A.: Accurate synthetic generation of realistic personal information. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 507–514. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    North Carolina State Board of Elections: NC voter registration database, (last accessed September 11, 2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peter Christen
    • 1
  • Ross W. Gayler
    • 2
  1. 1.Research School of Computer ScienceThe Australian National UniversityCanberraAustralia
  2. 2.VedaMelbourneAustralia

Personalised recommendations