DQM-Vocabulary Primer

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	* Automated consistency checks between data requirements		* Automated consistency checks between data requirements
	* Exchange of data quality information and data requirements on web-scale		* Exchange of data quality information and data requirements on web-scale
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		+	<div style="width:425px" id="__ss_7389488"> <strong style="display:block;margin:12px 0 4px"><a href="http://www.slideshare.net/cfuerber/towards-a-vocabulary-for-data-quality-management-in-semantic-web-architectures" title="Towards a Vocabulary for Data Quality Management in Semantic Web Architectures" target="_blank">Towards a Vocabulary for Data Quality Management in Semantic Web Architectures</a></strong> <iframe src="http://www.slideshare.net/slideshow/embed_code/7389488" width="425" height="355" frameborder="0" marginwidth="0" marginheight="0" scrolling="no"></iframe> <div style="padding:5px 0 12px"> View more <a href="http://www.slideshare.net/" target="_blank">presentations</a> from <a href="http://www.slideshare.net/cfuerber" target="_blank">Christian Fuerber</a> </div> </div>
	== Quick Start ==		== Quick Start ==

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Revision as of 11:58, 9 September 2011

Contents 1 Purpose and Usage of the DQM-Vocabulary 2 Quick Start 2.1 Create Data Requirements 2.2 Generate Problem and Score Annotations 2.3 Generate Problem Reports 2.4 Generate Data Quality Score Reports 3 Examples 4 Querying the DQM-Vocabulary

Purpose and Usage of the DQM-Vocabulary

The DQM-Vocabulary was created to support data quality management acitivities in Semantic Web architectures. It's major stregth is the ability to represent data requirements, i.e. quality-relevant expectations on data, so that computers can interpret the requirements and take further actions. Among other things, the DQM-Vocabulary enables the following core-features:

• Automated creation of problem reports and data quality scores based on data requirements
• Automated consistency checks between data requirements
• Exchange of data quality information and data requirements on web-scale

Quick Start

Create Data Requirements

Generate Problem and Score Annotations

Generate Problem Reports

Generate Data Quality Score Reports

Examples

• Creating Data Requirements
• Usage of DQM-Vocab as OWL DL or OWL Full

Querying the DQM-Vocabulary

• Data Quality Monitoring Reports
• Data Quality Assessment Reports