Assessing data reliability in an information system
Journal of Management Information Systems - Special Issue: Database Management
Toward quality data: an attribute-based approach
Decision Support Systems - Special issue on information technologies and systems
Anchoring data quality dimensions in ontological foundations
Communications of the ACM
Semiotics in information systems engineering
Semiotics in information systems engineering
Communications of the ACM - Supporting community and building social capital
Data Quality for the Information Age
Data Quality for the Information Age
Data Quality in Web Information Systems
ER '02 Proceedings of the 21st International Conference on Conceptual Modeling
Design and Analysis of Quality Information for Data Warehouses
ER '98 Proceedings of the 17th International Conference on Conceptual Modeling
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Discovering the semantics of relational tables through mappings
Journal on Data Semantics VII
Managing information quality in e-science: a case study in proteomics
ER'05 Proceedings of the 24th international conference on Perspectives in Conceptual Modeling
Clio: Schema Mapping Creation and Data Exchange
Conceptual Modeling: Foundations and Applications
Rationality of cross-system data duplication: a case study
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
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We address the fundamental question: what does it mean for data in a database to be of high quality? We motivate our discussion with examples, where traditional views on data quality are found to be unsatisfactory. Our work is founded on the premise that data values are primarily linguistic signs that convey meaning from their producer to their user through senses and referents. In this setting, data quality issues arise when discrepancies occur during this communication. We sketch a theory of senses for individual values in a relational table based on its semantics expressed using some ontology. We use this to offer a compositional approach, where data quality is expressed in terms of a variety of primitive relationships among values and their senses. We evaluate our approach by accounting for quality attributes in other frameworks proposed in the literature. This exercise allows us to (i) reveal and differentiate multiple, sometimes conflicting, definitions of a quality attribute, (ii) accommodate competing views on how these attributes are related, and (iii) point to possible new definitions.