Data Quality Management with Semantic Technologies
Data Quality Management with Semantic Technologies
Springer | Business Information Systems | January 12, 2016 | ISBN-10: 3658122242 | 205 pages | pdf | 4.4 mb
Springer | Business Information Systems | January 12, 2016 | ISBN-10: 3658122242 | 205 pages | pdf | 4.4 mb
Authors: Fürber, Christian
Publication in the field of economic sciences
Christian Fürber investigates the useful application of semantic technologies for the area of data quality management. Based on a literature analysis of typical data quality problems and typical activities of data quality management processes, he develops the Semantic Data Quality Management framework as the major contribution of this thesis. The SDQM framework consists of three components that are evaluated in two different use cases. Moreover, this thesis compares the framework to conventional data quality software. Besides the framework, this thesis delivers important theoretical findings, namely a comprehensive typology of data quality problems, ten generic data requirement types, a requirement-centric data quality management process, and an analysis of related work.
Number of Illustrations and Tables
63 illus.
Topics
Business Information Systems
Knowledge Management
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