Toward quality data: an attribute-based approach
Decision Support Systems - Special issue on information technologies and systems
Training with noise is equivalent to Tikhonov regularization
Neural Computation
Error reduction through learning multiple descriptions
Machine Learning
Not all answers are equally good: estimating the quality of database answers
Flexible query answering systems
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality-driven Integration of Heterogenous Information Systems
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Sample-Based Quality Estimation of Query Results in Relational Database Environments
IEEE Transactions on Knowledge and Data Engineering
Brokering infrastructure for minimum cost data procurement based on quality-quantity models
Decision Support Systems
A risk based model for quantifying the impact of information quality
Computers in Industry
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The relationship between data accuracy and the resulting information accuracy is of great interest in numerous problem domains. An understanding of this relationship can improve the efficiency of data management and increase the accuracy and utility of information in problem-solving settings. Nonetheless, our understanding of that relationship is still partial. In fact, even the sign of the relationship is not well understood. Nearly all researchers have embraced the popular belief in GIGO (Garbage In, Garbage Out), which indicates a strong positive link between input accuracy and output accuracy. However, there is evidence that hints to a more complex association. This article addresses the relationship between input accuracy and output accuracy, particularly the sign of that relationship, in satisficing decisions that apply a conjunctive or disjunctive rule for combining selected criteria. Analysis of a simple scenario shows a surprising result: the sign of that relationship varies; higher input accuracy can lead to lower output accuracy. This article derives criteria that determine the sign of the relationship and explains and illustrates conditions in which the sign is negative. The findings of this research imply certain rules for guiding data quality management resource allocation and design decisions in similar scenarios.