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Quality Data Objects
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.
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.
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.
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.
40 pages, Hardcover
First published August 5, 2015
About the author
Richard Y. Wang
14 booksRichard Y. Wang is Director of the MIT Chief Data Officer and Information Quality (CDOIQ) Program, and the Founder and Executive Director of the Institute for Chief Data Officers (iCDO) at University of Arkansas at Little Rock.
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