Economics-Driven Data Management: An Application to the Design of Tabular Data Sets
IEEE Transactions on Knowledge and Data Engineering
Brokering infrastructure for minimum cost data procurement based on quality-quantity models
Decision Support Systems
Impact of the Union and Difference Operations on the Quality of Information Products
Information Systems Research
Measuring information volatility in a health care information supply chain
Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology
Data quality assessment in context: A cognitive perspective
Decision Support Systems
Using Data Mining Techniques to Discover Bias Patterns in Missing Data
Journal of Data and Information Quality (JDIQ)
The Effects and Interactions of Data Quality and Problem Complexity on Classification
Journal of Data and Information Quality (JDIQ)
GIGO or not GIGO: The Accuracy of Multi-Criteria Satisficing Decisions
Journal of Data and Information Quality (JDIQ)
Reassessing Data Quality for Information Products
Management Science
Modeling the information completeness of object tracking systems
The Journal of Strategic Information Systems
Design of an information volatility measure for health care decision making
Decision Support Systems
Biases in multi-criteria, satisficing decisions due to data errors
Journal of Data and Information Quality (JDIQ)
A multidimensional analysis of data quality for credit risk management: New insights and challenges
Information and Management
Data Quality of Query Results with Generalized Selection Conditions
Operations Research
A risk based model for quantifying the impact of information quality
Computers in Industry
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The cost associated with making decisions based on poor-quality data is quite high. Consequently, the management of data quality and the quality of associated data management processes has become critical for organizations. An important first step in managing data quality is the ability to measure the quality of information products (derived data) based on the quality of the source data and associated processes used to produce the information outputs. We present a methodology to determine two data quality characteristics--accuracy and completeness--that are of critical importance to decision makers. We examine how the quality metrics of source data affect the quality for information outputs produced using the relational algebra operations selection, projection, and Cartesian product. Our methodology is general, and can be used to determine how quality characteristics associated with diverse data sources affect the quality of the derived data.