Context-based Data Quality Metrics in Data Warehouse Systems

The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in researc...
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Autor*in:	Flavia Serra [verfasserIn] Adriana Marotta [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Übergeordnetes Werk:	In: CLEI Electronic Journal - Centro Latinoamericano de Estudios en Informática, 2008, 20(2017), 2, Seite 3:1-3:23
Übergeordnetes Werk:	volume:20 ; year:2017 ; number:2 ; pages:3:1-3:23

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.19153/cleiej.20.2.3

Katalog-ID:	DOAJ032223846

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title	Context-based Data Quality Metrics in Data Warehouse Systems
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title_sort	context-based data quality metrics in data warehouse systems
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title_auth	Context-based Data Quality Metrics in Data Warehouse Systems
abstract	The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in research and industry. DQ in this kind of systems becomes essential. This work presents a proposal for identifying DQ problems in the domain of DWS, considering the different contexts that exist in each system component. This proposal may act as a first conceptual framework that guides the DQ-responsible in the management of DQ in DWS. The main contributions of this work are a thorough literature review about how contexts are used for evaluating DQ in DWS, and a proposal for assessing DQ in DWS through context-based DQ metrics.
abstractGer	The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in research and industry. DQ in this kind of systems becomes essential. This work presents a proposal for identifying DQ problems in the domain of DWS, considering the different contexts that exist in each system component. This proposal may act as a first conceptual framework that guides the DQ-responsible in the management of DQ in DWS. The main contributions of this work are a thorough literature review about how contexts are used for evaluating DQ in DWS, and a proposal for assessing DQ in DWS through context-based DQ metrics.
abstract_unstemmed	The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in research and industry. DQ in this kind of systems becomes essential. This work presents a proposal for identifying DQ problems in the domain of DWS, considering the different contexts that exist in each system component. This proposal may act as a first conceptual framework that guides the DQ-responsible in the management of DQ in DWS. The main contributions of this work are a thorough literature review about how contexts are used for evaluating DQ in DWS, and a proposal for assessing DQ in DWS through context-based DQ metrics.
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