Approximate data instance matching: a survey

• Published in: Knowledge and information systems Vol. 27; no. 1; pp. 1 - 21
• Main Authors: Dorneles, Carina Friedrich, Gonçalves, Rodrigo, dos Santos Mello, Ronaldo
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.04.2011; Springer; Springer Nature B.V
• Subjects: Applied sciences; Approximation; Artificial intelligence; Computer Science; Computer science; control theory; systems; Data base management; Data Mining and Knowledge Discovery; Database Management; Exact sciences and technology; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Information systems. Data bases; IT in Business; Matching; Memory organisation. Data processing; Queries; Relational data bases; Semantics; Similarity; Similarity measures; Software; Speech and sound recognition and synthesis. Linguistics; Studies; Survey Paper; Taxonomy; Terminology; XML; Duplicate detection; Object matching; Instance data matching; Similarity matching; Similarity function; Record linkage; Entity resolution; Record matching; Information integration; Tree(graph); Terminology; Similarity; Character string; Semantics; Classification; Database; Linguistics; Data structure; Data link; Nearest neighbour; XML language; Cleaning; Date; Value function; Semistructured data; Document structure; Artificial intelligence
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-010-0285-0

Approximate data matching is a central problem in several data management processes, such as data integration, data cleaning, approximate queries, similarity search and so on. An approximate matching process aims at defining whether two data represent the same real-world object. For atomic values (strings, dates, etc), similarity functions have been defined for several value domains (person names, addresses, and so on). For matching aggregated values, such as relational tuples and XML trees, approaches alternate from the definition of simple functions that combine values of similarity of record attributes to sophisticated techniques based on machine learning, for example. For complex data comparison, including structured and semistructured documents, existing approaches use both structure and data for the comparison, by either considering or not considering data semantics. This survey presents terminology and concepts that base approximated data matching, as well as discusses related work on the use of similarity functions in such a subject.