Titlebook: Data Quality Management with Semantic Technologies; Christian Fürber Book 2016 Springer Fachmedien Wiesbaden 2016 Data Quality Problems.Da

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書目名稱Data Quality Management with Semantic Technologies影響因子(影響力)




書目名稱Data Quality Management with Semantic Technologies影響因子(影響力)學(xué)科排名




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書目名稱Data Quality Management with Semantic Technologies網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Quality Management with Semantic Technologies被引頻次




書目名稱Data Quality Management with Semantic Technologies被引頻次學(xué)科排名




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書目名稱Data Quality Management with Semantic Technologies年度引用學(xué)科排名




書目名稱Data Quality Management with Semantic Technologies讀者反饋




書目名稱Data Quality Management with Semantic Technologies讀者反饋學(xué)科排名

你敢命令

吞沒

ascetic

https://doi.org/10.1057/9780230113442rk. Ontology-based data quality management frameworks in here are artifacts that make use of ontologies to support data quality management activities. In the following, we provide a high-level classification of the field, which is then used to organize the presentation of related work in this chapter.

escalate

https://doi.org/10.1007/978-3-030-64290-7In this chapter, we will provide a brief introduction into the thesis topic, clarify our understanding of the term “data” and its dependency to business processes and decisions, and discuss the economic relevance of the systematic management of data quality. Moreover, we give a short overview of the thesis structure.

xanthelasma

xanthelasma

GILD

Application Procedure of SDQM,In this chapter, we explain how to use the SDQM architecture from the perspective of business users who want to create data requirements, identify data requirement violations, and evaluate the quality state based on their data requirements.

良心

罵人有污點(diǎn)

Comparative and International Educationg & Strong, 1996, p. 22f.) that can be defined from several different perspectives (cf. Ge & Helfert, 2007, p. 1; Kahn et al., 2002, p. 185). For example, data consumers, data producers, data providers, and data custodians may all have different perspectives on the definition of data quality (cf. Ka