Original scientific paper

INDEPENDENT DE-DUPLICATION IN DATA CLEANING

Ajumobi Udechukwu ; Dept. of Computer Science, University of Calgary, Canada
Christie Ezeife ; School of Computer Science, University of Windsor, Canada
Ken Barker ; Dept. of Computer Science, University of Calgary, Canada

Abstract

Many organizations collect large amounts of data to support their business and decision-making processes. The data originate from a variety of sources that may have inherent data-quality problems. These problems become more pronounced when heterogeneous data sources are integrated (for example, in data warehouses). A major problem that arises from integrating different databases is the existence of duplicates. The challenge of de-duplication is identifying “equivalent” records within the database. Most published research in de-duplication propose techniques that rely heavily on domain knowledge. A few others propose solutions that are partially domain-independent. This paper identifies two levels of domain-independence in de-duplication namely: domain-independence at the attribute level, and domain-independence at the record level. The paper then proposes a positional algorithm that achieves domain-independent de-duplication at the attribute level, and a technique for field weighting by data profiling, which, when used with the positional algorithm, achieves domain-independence at the record level. Experiments show that the proposed techniques achieve more accurate de-duplication than the existing algorithms.

Keywords

Data cleaning; De-duplication; data quality; field-matching; record linkage

Hrčak ID:

78279

URI

https://hrcak.srce.hr/78279

Publication date:

21.12.2005.

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