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By Yang W. Lee, Leo L. Pipino, James D. Funk, Richard Y. Wang

Facts caliber offers an exposé of analysis and perform within the info caliber box for technically orientated readers. it really is in keeping with the examine carried out on the MIT overall facts caliber administration (TDQM) application and paintings from different prime examine associations. This publication is meant essentially for researchers, practitioners, educators and graduate scholars within the fields of laptop technological know-how, info know-how, and different interdisciplinary parts. It varieties a theoretical origin that's either rigorous and correct for facing complex matters regarding facts caliber. Written with the target to supply an summary of the cumulated examine effects from the MIT TDQM examine viewpoint because it pertains to database study, this booklet is a superb creation to Ph.D. who desire to extra pursue their study within the facts caliber zone. it's also a very good theoretical advent to IT pros who desire to achieve perception into theoretical leads to the technically-oriented info caliber zone, and follow a number of the key thoughts to their perform.

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Extra resources for Data Quality

Sample text

To achieve dataquality-by-design, it would be useful to incorporate data quality attributes at the conceptual design stage of a database application. Conventional conceptual data models and their corresponding design methodologies, however, have been developed to capture entities, relationships, attributes, and other advanced concepts such as is-a and component-of relationships. Data quality is not explicitly recognized. The task of incorporating data quality into the design of a database application is left to the designer.

Application quality requirements and data quality requirements can be identified in a similar manner; that is, by the de- 40 Extending the ER Model to Represent Data Quality Requirements Chapter 3 signer working with the user to elicit the requirements and possibly suggesting some quality requirements based upon the designer's experience with similar or relatedapplicationdomains. Wang and Strong [12] 0provide a framework that categorizes data quality into four main categories: 1) intrinsic data quality (believability, accuracy, objectivity, reputation), 2) contextual data quality (value-added, relevancy, timeliness, completeness, appropriate amount ofdata), 3), representation data quality (interpretability, ease of understanding, representational consistency, concise representation), and 4) accessibility data quality (accessibility, access security).

Projection Projection is a unary operation that selects a vertical subset of a quality relation based on the set of attributes specified in the Projection operation. The result includes the projected quality relation and the corresponding quality indicator relations that are associated with the set of attributes specified in the Projection operation. a =m t1•a))} Union In this operation, the two operand quality relations must be QI-Compatible. The result includes (1) tuples from both qr and qs after elimination of duplicates, and (2) the corresponding quality indicator relations that are associated with the resulting tuples.

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