Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
AIMQ: a methodology for information quality assessment
Information and Management
Modeling Completeness versus Consistency Tradeoffs in Information Decision Contexts
IEEE Transactions on Knowledge and Data Engineering
Addressing Timeliness/Accuracy/Cost Tradeoffs in Information Collection for Dynamic Environments
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Discovering data quality rules
Proceedings of the VLDB Endowment
Methodologies for data quality assessment and improvement
ACM Computing Surveys (CSUR)
Handbook on Decision Support Systems 2: Variations
Handbook on Decision Support Systems 2: Variations
Introduction to Information Quality
Introduction to Information Quality
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A conceptual framework for the automatic discovery of dependencies between data quality dimensions is described. Dependency discovery consists in recovering the dependency structure for a set of data quality dimensions measured on attributes of a database. This task is accomplished through the data mining methodology, by learning a Bayesian Network from a database. The Bayesian Network is used to analyze dependency between data quality dimensions associated with different attributes. The proposed framework is instantiated on a real world database. The task of dependency discovery is presented in the case when the following data quality dimensions are considered; accuracy, completeness, and consistency. The Bayesian Network model shows how data quality can be improved while satisfying budget constraints.