Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Communications of the ACM
Data quality and systems theory
Communications of the ACM
Data quality: the field guide
Communications of the ACM - Supporting community and building social capital
Data mining standards initiatives
Communications of the ACM - Evolving data mining into solutions for insights
AIMQ: a methodology for information quality assessment
Information and Management
An Algebraic Approach to Data Mining: Some Examples
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Exploratory Data Mining and Data Cleaning
Exploratory Data Mining and Data Cleaning
Intelligence management and complexities: a case study approach
Proceedings of the 9th ACM SIGCHI Italian Chapter International Conference on Computer-Human Interaction: Facing Complexity
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We introduce a framework for improving information quality in complex distributed systems that integrates: 1) Analytic models that describe baseline values for attributes and combinations of attributes and components that detect statistically significant changes from baselines. These models determine whether a significant change has occurred, and if so, when. 2) Casual models that help determine why a statistically significant change has occurred and what its impact is. These models focus on the reasons for a change. 3) Formal business and technical reference models so that data and information quality problems are less likely to occur in the future. In this note, we focus on the first two types of models and describe how this framework applies to data quality problems associated with electronic payments transactions and highway traffic patterns.