The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Anchoring data quality dimensions in ontological foundations
Communications of the ACM
A product perspective on total data quality management
Communications of the ACM
Improving data warehouse and business information quality: methods for reducing costs and increasing profits
WWW '99 Proceedings of the eighth international conference on World Wide Web
Data Quality for the Information Age
Data Quality for the Information Age
Fundamentals of Data Warehouses
Fundamentals of Data Warehouses
Managing Information Quality
Information Systems - Special issue: Data quality in cooperative information systems
Completeness of integrated information sources
Information Systems - Special issue: Data quality in cooperative information systems
Time-Related Factors of Data Quality in Multichannel Information Systems
Journal of Management Information Systems
Quality-driven query answering for integrated information systems
Quality-driven query answering for integrated information systems
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Quality of information benefits both on line transactional processing and on line analytical processing. However, quality assurance processes are mostly human intensive and the literature provides limited support to their automation. This paper proposes a rule-based data monitoring and improvement approach as a first step towards self-management of quality of data. These rules specify when to trigger both assessment procedures and improvement actions (e.g. data cleaning), on the basis of the actions performed on the databases and specific quality requirements associated with queries performed by users. They also capture all the events occurring as a consequence of data quality problems and alert the Quality Administrator if human involvement is required. Rules are classified and formalized in the paper. The overall data quality monitoring and improvement process is explained with examples.