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ThinkMind // DBKDA 2021, The Thirteenth International Conference on Advances in Databases, Knowledge, and Data Applications // View article dbkda_2021_1_30_50019


Visualization of Multi-Level Data Quality Dimensions with QuaIIe

Authors:
Sheny Illescas Martinez
Lisa Ehrlinger
Wolfram Wöß

Keywords: Data visualization; Data quality, Data quality dimensions; Graphical user interface.

Abstract:
Data quality assessment is a challenging but necessary task to ensure that business decisions that are derived from data can be trusted. A number of data quality metrics have been developed to measure dimensions like accuracy, completeness, and timeliness. The tool QuaIIe (developed in our previous research) facilitates the calculation of different data quality metrics on both, schema- and data-level, and for heterogeneous information systems. However, to gain meaningful results from the automatically calculated metrics, it is key that humans understand the results of these metrics. This understanding is specifically important when contextual information needs to be considered, which is not encoded in the data. In this paper, we present a visualization approach to enable human-centered data quality assessment across multiple dimensions and arbitrary complex data sources. The approach has been implemented as graphical user interface in QuaIIe.

Pages: 15 to 20

Copyright: Copyright (c) IARIA, 2021

Publication date: May 30, 2021

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-857-0

Location: Valencia, Spain

Dates: from May 30, 2021 to June 3, 2021

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