Data Quality by Y. Richard Wang Richard Y. Wang Mostapha Ziad

By Y. Richard Wang Richard Y. Wang Mostapha Ziad

Information caliber offers an expos? of analysis and perform within the facts caliber box for technically orientated readers. it really is in line with the examine performed on the MIT overall facts caliber administration (TDQM) software and paintings from different best examine associations. This e-book is meant basically for researchers, practitioners, educators and graduate scholars within the fields of laptop technology, info expertise, and different interdisciplinary parts. It kinds a theoretical starting place that's either rigorous and suitable for facing complex matters regarding info caliber. Written with the aim to supply an summary of the cumulated examine effects from the MIT TDQM learn standpoint because it pertains to database learn, this e-book is a superb advent to Ph.D. who desire to extra pursue their study within the info caliber region. it really is additionally a good theoretical advent to IT pros who desire to achieve perception into theoretical ends up in the technically-oriented info caliber zone, and practice the various key thoughts to their perform.

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It would only be appropriate if the interpretation of rating is obvious, but this is not usually the case. Further analysis of this and alternative representations is provided in Storey and Wang [6]. In general, the Attribute Gerund Representation can be applied when an application entity attribute needs to be associated directly with a data quality entity that has various quality categories, each of which has a corresponding Rating and “description” (or other data). 3: Attribute Gerund Representation Conceptual Design Example This section extends the training database example to incorporate data on course standards along with other quality requirements.

This capability is very useful in an interoperable environment where data in different databases have differentsemantics. • Quality indicators associated with an attribute may facilitate a better interpretation ofnull values. • In a data quality control process, when errors are detected, the data administrator can identify the source of error by examining quality indicators such as data source or collection method. Data Integrity A fundamental property of the attribute-based model is that an attribute value and its corresponding quality indicator values (including all descendants) are treated as an atomic unit.

Qi ) map the quality indicators to unique positions in the 11 quality indicator tree. , to specify all the elements of s). To alleviate this situation, we introduce i-level QI-compatibility (i-level QIV-Equal) as a special case for QI-compatibility (QIV-equal) in which all the quality indicators up to a certain level of depth in a quality indicator tree are considered. Let a1 and a2 be two application attributes. Let a1 and a2 be QI-Compatible with respect to S. Let w1 and w2 be values of a1 and a2 respectively, then w1 and w2 are defined to be i-level QI-Compatible if the following two conditions are satisfied: (1) a1 and a 2 are QI-Compatible with respect to S, and (2) S consists of all quality indicators present within i levels ofthe quality indicator tree of a1 (thus ofa2).

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