Record linkage: similarity measures and algorithms

ABSTRACT

This tutorial provides a comprehensive and cohesive overview of the key research results in the area of record linkage methodologies and algorithms for identifying approximate duplicate records, and available tools for this purpose. It encompasses techniques introduced in several communities including databases, information retrieval, statistics and machine learning. It aims to identify similarities and differences across the techniques as well as their merits and limitations.

REFERENCES

• 1: C. Batini, T. Catarci, and M. Scannapieco. A survey of data quality issues in cooperative information systems. Pre-conference ER tutorial, 2004.
• 2: Theodore Johnson , Tamraparni Dasu, Data quality and data cleaning: an overview, Proceedings of the 2003 ACM SIGMOD international conference on Management of data, June 09-12, 2003, San Diego, California [doi>10.1145/872757.872875]
• 3: Nick Koudas , Divesh Srivastava, Approximate joins: concepts and techniques, Proceedings of the 31st international conference on Very large data bases, August 30-September 02, 2005, Trondheim, Norway

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

General Terms:
Algorithms, Experimentation

Keywords:
approximate join, data quality