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Author Geisler, Sandra ♦ Quix, Christoph ♦ Weber, Sven ♦ Jarke, Matthias
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Data streams ♦ Data quality assessment ♦ Data quality control ♦ Ontologies
Abstract Data Stream Management Systems (DSMS) provide real-time data processing in an effective way, but there is always a tradeoff between data quality (DQ) and performance. We propose an ontology-based data quality framework for relational DSMS that includes DQ measurement and monitoring in a transparent, modular, and flexible way. We follow a threefold approach that takes the characteristics of relational data stream management for DQ metrics into account. While (1) Query Metrics respect changes in data quality due to query operations, (2) Content Metrics allow the semantic evaluation of data in the streams. Finally, (3) Application Metrics allow easy user-defined computation of data quality values to account for application specifics. Additionally, a quality monitor allows us to observe data quality values and take counteractions to balance data quality and performance. The framework has been designed along a DQ management methodology suited for data streams. It has been evaluated in the domains of transportation systems and health monitoring.
Description Author Affiliation: RWTH Aachen University, Aachen, Germany (Geisler, Sandra; Weber, Sven); Fraunhofer Institute for Applied Information Technology and RWTH Aachen University, Sankt Augustin, Germany (Quix, Christoph); RWTH Aachen University and Fraunhofer Institute for Applied Information Technology, Aachen, Germany (Jarke, Matthias)
ISSN 19361955
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-10-06
Publisher Place New York
e-ISSN 19361963
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 7
Issue Number 4
Page Count 34
Starting Page 1
Ending Page 34

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Source: ACM Digital Library