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Tabular Data Anomaly Patterns

Tabular Data Anomaly Patterns

Kategori
Vitenskapelig Kapittel/Artikkel/Konferanseartikkel
Sammendrag
One essential and challenging task in data science is data cleaning - the process of identifying and eliminating data anomalies. Different data types, data domains, data acquisition methods, and final purposes of data cleaning have resulted in different approaches in defining data anomalies in the literature. This paper proposes and describes a set of basic data anomalies in the form of anomaly patterns commonly encountered in tabular data, independently of the data domain, data acquisition technique, or the purpose of data cleaning. This set of anomalies can serve as a valuable basis for developing and enhancing software products that provide general-purpose data cleaning facilities and can provide a basis for comparing different tools aimed to support tabular data cleaning capabilities. Furthermore, this paper introduces a set of corresponding data operations suitable for addressing the identified anomaly patterns and introduces Grafterizer - a software framework that implements those data operations
Oppdragsgiver
  • EC/H2020 / 732590
  • EC/H2020 / 644497
  • EC/H2020 / 732003
Språk
Engelsk
Forfatter(e)
Institusjon(er)
  • SINTEF Digital / Software and Service Innovation
År
2017
Forlag
IEEE
Bok
2017 International Conference on Big Data Innovations and Applications (Innovate-Data), Prague, Czech Republic, Czech Republic, 21-23 Aug. 2017
ISBN
978-1-5386-0960-6
Side(r)
25 - 34