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{ "item_title" : "Data Quality Management with Semantic Technologies", "item_author" : [" Christian Fürber "], "item_description" : "Christian F rber 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. ", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/65/812/224/3658122242_b.jpg", "price_data" : { "retail_price" : "89.99", "online_price" : "89.99", "our_price" : "89.99", "club_price" : "89.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Data Quality Management with Semantic Technologies|Christian Fürber

Data Quality Management with Semantic Technologies

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Overview

Christian F rber 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.

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Details

  • ISBN-13: 9783658122249
  • ISBN-10: 3658122242
  • Publisher: Springer Gabler
  • Publish Date: January 2016
  • Dimensions: 8.27 x 5.83 x 0.54 inches
  • Shipping Weight: 0.69 pounds
  • Page Count: 205

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