A qualitative approach to automatic data quality judgment

Jang, Yeona; Ishii, Alexander T.; Wang, Richard Y.

doi:10.1080/10919399509540245

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…An attribute-based model that can be used to incorporate quality aspects of data products has also been developed [57], [115], [116], [117], [119]. Underlying this model is the idea that objective data quality indicators (such as source, time, and collection method) can be designed into a system in such a way that they will be delivered along with the data products.…”

Section: Analysis and Design Of The Quality Aspects Of Data Promentioning

confidence: 99%

“…Three measures for data reliability are developed: 1) internal reliability (the "commonly accepted" characteristics of data items), 2) relative reliability (the compliance of data to user requirements), and 3) absolute reliability (the level of resemblance of data items to reality). Jang, Ishii, and Wang [57] propose a data quality reasoner that provides both an automated form of judging data quality and an objective measure of overall data quality.…”

Section: C3 Dimensions Of Data Quality and Measurement Of Their Valuesmentioning

confidence: 99%

“…Theattribute-based approach [57], [87], [115], [116], [117], [119] can also be applied to verify the quality of data. By examining data quality indicators (such as source, time, and procurement method) or data quality parameters (such as timeliness, credibility, and completeness), the producer can verify, at different stages of the data product manufacturing process, whether the data conform with the specified requirements.…”

Section: Productionmentioning

confidence: 99%

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A framework for analysis of data quality research

Wang¹,

Storey

Firth³

1995

IEEE Trans. Knowl. Data Eng.

478

173

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Abstiuct-Organizational databases are pervaded with data of poor quality. However, there has not been an analysis of the data quality literature that provides an overall understanding of the state-of-art research in this area. Using an analogy between product manufacturing and data manufacturing, this paper develops a framework for analyzing data quality research, and uses it as the basis for organizing the data quality literature. This framework consists of seven elements: management responsibilities, operation and assurance costs, research and development, production, distribution, personnel management, and legal function. The analysis reveals that most research efforts focus on operation and assurance costs, research and development, and production of data products. Unexplored research topics and unresolved issues are identified and directions for future research provided.

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Section: Analysis and Design Of The Quality Aspects Of Data Promentioning

confidence: 99%

Section: C3 Dimensions Of Data Quality and Measurement Of Their Valuesmentioning

confidence: 99%

Section: Productionmentioning

confidence: 99%

See 1 more Smart Citation

A framework for analysis of data quality research

Wang¹,

Storey

Firth³

1995

IEEE Trans. Knowl. Data Eng.

478

173

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“…We summarize in this sub-section research that we have performed in [32][33][34][35]. In a simple homogeneous database management system environment, data consumers are generally knowledgeable about the characteristics of the data they use.…”

Section: Data Quality Reasonermentioning

confidence: 99%

“…Unfortunately, the vast amount of data such as those in the TIA context would make it difficult to analyze such data and draw useful conclusions about overall data quality for subsequent analysis, recommendation and decision activities. In an attempt to assist data consumers in judging if the "quality" of data meets a data consumer's requirements, we propose a framework for deriving an overall data quality level from various local relationships between the factors, which affect data quality [33,35]. We focus on the problem of assessing levels of data quality, i.e., the degree to which data characteristics match those required by a data consumer.…”

Section: Data Quality Reasonermentioning

confidence: 99%

An information product approach for total information awareness

Wang

Allen

Harris

et al.

2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652)

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Abstract--To fight terrorism successfully, the quality of data must be considered to avoid garbage-in-garbage-out. Research has shown that data quality (DQ) goes beyond accuracy to include dimensions such as believability, timeliness, and accessibility. In collecting, processing, and analyzing a much broader array of data than we do currently, therefore, a comprehensive approach must be developed to ensure that DQ is incorporated in determining the most probable current or future scenario for preemption, national security warning and decision making. Additional data such as who was the data source, when was the data made available, how, where, and why also need to be included to judge the quality of the information assembled from these data.We propose such an approach for Total Information Awareness with Quality (TIAQ), which includes concepts, models, and tools. Central to our approach is to manage information as a product with four principles. We have applied the information product approach to research sites where opportunities arise. For example, the Air Force Material Command uses requirements definition and forecasting processes to perform a number of functions. However, the Air Force experienced several complex problems due to DQ problems; as a result, fuel pumps were unavailable. Each engine needs a fuel pump; when a pump is not available, a military aircraft is grounded. We traced the fuel-pump throughout the process of remanufacture, and identified root causes such as delays by pump contractors and ordering problems. To a certain extent, detecting foreign terrorists and decipher their plots are analogous to tracing fuel pumps. Our research provides an interdisciplinary approach to facilitating Total Information Awareness.

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Advances in Database Systems

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A qualitative approach to automatic data quality judgment

Cited by 6 publications

References 4 publications

A framework for analysis of data quality research

A framework for analysis of data quality research

An information product approach for total information awareness

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