Association for Computing Machinery
Data quality is critical for many information-intensive applications. One of the best opportunities to improve data quality is during entry. USHER provides a theoretical, data-driven foundation for improving data quality during entry. Based on prior data, USHER learns a probabilistic model of the dependencies between form questions and values. Using this information, USHER maximizes information gain. By asking the most unpredictable questions first, USHER is better able to predict the answers for the remaining questions.