A product perspective on total data quality management
Communications of the ACM
Data Quality for the Information Age
Data Quality for the Information Age
A Framework for Analysis of Data Quality Research
IEEE Transactions on Knowledge and Data Engineering
A Classification and Summary of Software Evaluation and Selection Methodologies
A Classification and Summary of Software Evaluation and Selection Methodologies
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
An argument-based multi-agent system for information integration
ArgMAS'10 Proceedings of the 7th international conference on Argumentation in Multi-Agent Systems
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Due to its costly impact, data quality is becoming an emerging domain of research. Motivated by its stakes and issues, especially in the application domain of Technological Intelligence, we propose a generic methodology for modeling and managing data quality in the context of multiple information sources. Data quality has different categories of quality criteria and their evaluations enable the detection of errors and poor quality data. We introduce the notion of relative data quality when several data describe the same entity in the real world but have contradictory values : homologous data. Our approach differs from the general approach for resolving extensional inconsistencies in integration of heterogeneous systems. We cumulatively store homologous data and their quality metadata and we recommend dynamically data with the best quality and data which are the most appropriate to a particular user. A value recommendation algorithm is proposed and applied to the Technological Intelligence application domain.