Analysis of Data Quality Problem Taxonomies

Arturs Zogla, Inga Meirane, Edgars Salna

2015

Abstract

There are many reasons to maintain high quality data in databases and other structured data sources. High quality data ensures better discovery, automated data analysis, data mining, migration and re-use. However, due to human errors or faults in data systems themselves data can become corrupted. In this paper existing data quality problem taxonomies for structured textual data and several improvements are analysed. A new classification of data quality problems and a framework for detecting data errors both with and without data operator assistance is proposed.

Paper Citation

in Harvard Style

Zogla A., Meirane I. and Salna E. (2015). Analysis of Data Quality Problem Taxonomies . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-097-0, pages 445-450. DOI: 10.5220/0005462604450450

in Bibtex Style

@conference{iceis15,
author={Arturs Zogla and Inga Meirane and Edgars Salna},
title={Analysis of Data Quality Problem Taxonomies},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2015},
pages={445-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005462604450450},
isbn={978-989-758-097-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Analysis of Data Quality Problem Taxonomies
SN - 978-989-758-097-0
AU - Zogla A.
AU - Meirane I.
AU - Salna E.
PY - 2015
SP - 445
EP - 450
DO - 10.5220/0005462604450450