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Identification of Real-world Objects in Multiple Databases

  • Mattis Neiling
Conference paper
  • 1.6k Downloads
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Object identification is an important issue for integration of data from different sources. The identification task is complicated, if no global and consistent identifier is shared by the sources. Then, object identification can only be performed through the identifying information, the objects data provides itself. Unfortunately real-world data is dirty, hence identification mechanisms like natural keys fail mostly — we have to take care of the variations and errors of the data. Consequently, object identification can no more be guaranteed to be fault-free. Several methods tackle the object identification problem, e.g. Record Linkage, or the Sorted Neighborhood Method.

Based on a novel object identification framework, we assessed data quality and evaluated different methods on real data. One main result is that scalability is determined by the applied preselection technique and the usage of efficient data structures. As another result we can state that Decision Tree Induction achieves better correctness and is more robust than Record Linkage.

Keywords

Association Rule Matched Pair Record Linkage Random Pair Multiple Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Mattis Neiling
    • 1
  1. 1.Technische Universität BerlinBerlin

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