Measuring and modelling data quality for quality-awareness in data mining

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Citations (Scopus)

Abstract

This chapter presents an overview of data quality management, data linkage and data cleaning techniques that can be advantageously employed for improving the quality awareness of the knowledge discovery process. Based on this database-oriented overview of data quality management, this chapter also presents a pragmatic step-by-step framework for data quality awareness and enhancement before warehousing and during the knowledge discovery process. Each step may use, combine and exploit the data quality characterization, measurement and management methods, and the related techniques proposed in the literature.

Original languageEnglish
Title of host publicationQuality Measures in Data Mining
EditorsFabrice Guillet, Howard Hamilton
Pages101-126
Number of pages26
DOIs
Publication statusPublished - 29 Jan 2007

Publication series

NameStudies in Computational Intelligence
Volume43
ISSN (Print)1860-949X

Keywords

  • Data cleaning
  • Data quality management
  • Data quality metadata
  • Record linkage

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Berti-Équille, L. (2007). Measuring and modelling data quality for quality-awareness in data mining. In F. Guillet, & H. Hamilton (Eds.), Quality Measures in Data Mining (pp. 101-126). (Studies in Computational Intelligence; Vol. 43). https://doi.org/10.1007/978-3-540-44918-8_5