Quality Data Objects (Classic Reprint)

Richard Y. Wang, M. P. Reddy

ISBN 10: 1332277705 / ISBN 13: 9781332277704

Editore: Forgotten Books, 2018

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Print on Demand. This book introduces the concept of the quality data object to explore how data can be enriched with quality information. The author argues that conventional database management systems do not provide sufficient functionality to ensure data quality from the end-user's perspective. The book investigates how to associate data with quality information that can help users make judgments of data quality for their specific application. The author proposes a set of quality measure methods that compute quality dimension values (such as accuracy, consistency, completeness, and timeliness), and a set of quality algebraic methods that supports the manipulation of quality data objects. The book is a first step toward the design and manufacture of data products. It will enable users to measure the quality of data products according to their chosen criteria and purchase data products based on their quality requirements. The concepts of quality data objects and quality data products aim to improve data quality and data reusability. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book.

Codice articolo 9781332277704_0

Dati bibliografici

• Titolo: Quality Data Objects (Classic Reprint)
• Editore: Forgotten Books
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 1332277705
• ISBN 13: 9781332277704
• Rilegatura: Paperback
• Condizione: New
• Tipo di libro: print-on-demand item
• Cataloghi dei venditori:
  • Logic

Descrizione articolo

Riassunto

Excerpt from Quality Data Objects

An attribute-based research that facilitates cell-level tagging of data has been proposed to enable users to retrieve data that conforms with their quality requirements (wang, Kon, 6: Madnick, 1993; Wang, Reddy, 8: Kon, 1992; Wang 6: Madnick, Included in this attribute-based research effort are a methodology for analyzing data quality requirements that extends the ER model proposed by Chen (chen, 1976; Chen, 1984; Chen, 1991; Chen Li, an attribute-based model encompassing a model description, a set of quality integrity rules, and a quality indicator algebra that extends the relational model proposed by Codd (codd, 1970; Codd, 1979; Codd, 1982; Codd, The quality indicator algebra can be used to process sql queries that are augmented with quality indicator requirements. From these quality indicators, the user can make a better judgment of the quality of data. The problem with this research is twofold: (1) In order to associate the application data with its corresponding quality description through the join Operation in the model, an artificial link needs to be created through the concept of quality key. (2) In order to be able to judge the quality of data, it is necessary to compute data quality dimension values and other procedure-oriented quality measures. Although these could be accomplished using the relational approach, it is not as natural compared to that of the object-oriented approach. Moreover, this research did not address issues involved in measuring data quality dimension values.

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This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.