Evaluating Software Complexity Measures
IEEE Transactions on Software Engineering
An Entropy-Based Measure of Software Complexity
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Understanding Quality in Conceptual Modeling
IEEE Software
Toward quality data: an attribute-based approach
Decision Support Systems - Special issue on information technologies and systems
Computer related risks
Anchoring data quality dimensions in ontological foundations
Communications of the ACM
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Data Quality for the Information Age
Data Quality for the Information Age
AIMQ: a methodology for information quality assessment
Information and Management
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the 25th International Conference on Software Engineering
ICSE '76 Proceedings of the 2nd international conference on Software engineering
ACM SIGMOD Record
AI Magazine - Special issue on semantic integration
Semantic-integration research in the database community
AI Magazine - Special issue on semantic integration
Data & Knowledge Engineering - Special issue: Quality in conceptual modeling
Coupling Metrics for Ontology-Based Systems
IEEE Software
Process models representing knowledge for action: a revised quality framework
European Journal of Information Systems - Special issue: Action in language, organisations and information systems
IEEE Intelligent Systems
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
A semiotic metrics suite for assessing the quality of ontologies
Data & Knowledge Engineering - Special issue: Natural language and database and information systems: NLDB 03
Supporting data quality management in decision-making
Decision Support Systems
Improving data quality through effective use of data semantics
Data & Knowledge Engineering - Special issue: WIDM 2004
Matching large schemas: Approaches and evaluation
Information Systems
A framework for information quality assessment
Journal of the American Society for Information Science and Technology
Eight key issues for the decision support systems discipline
Decision Support Systems
Information integration in the enterprise
Communications of the ACM - Enterprise information integration: and other tools for merging data
A user-centered functional metadata evaluation of moving image collections
Journal of the American Society for Information Science and Technology
Overview and Framework for Data and Information Quality Research
Journal of Data and Information Quality (JDIQ)
Semantic oriented ontology cohesion metrics for ontology-based systems
Journal of Systems and Software
Automatic evaluation of metadata quality in digital repositories
International Journal on Digital Libraries
What Makes a Good Ontology? A Case-Study in Fine-Grained Knowledge Reuse
ASWC '09 Proceedings of the 4th Asian Conference on The Semantic Web
Measuring design complexity of semantic web ontologies
Journal of Systems and Software
Evaluating a model for cost-effective data quality management in a real-world CRM setting
Decision Support Systems
Measuring the quality of an integrated schema
ER'10 Proceedings of the 29th international conference on Conceptual modeling
A conceptual modeling quality framework
Software Quality Control
Ontology engineering: a reality check
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
Improving financial data quality using ontologies
Decision Support Systems
Data quality: Setting organizational policies
Decision Support Systems
Interoperability of XBRL Financial Statements in the U.S.
International Journal of E-Business Research
Normal accidents: Data quality problems in ERP-enabled manufacturing
Journal of Data and Information Quality (JDIQ)
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Data standards are often used by multiple organizations to produce and exchange data. Given the high cost of developing data standards and their significant impact on the interoperability of data produced using the standards, the quality of data standards must be systematically measured. We develop a framework for systematically assessing the quality of large-scale data standards using automated tools. It consists of metrics for intrinsic and contextual quality dimensions, as well as effectual metrics that assess the extent to which a standard enables data interoperability. We evaluate the quality assessment framework using two versions of a large financial reporting standard, the US GAAP Taxonomy, and public companies' financial statements created using the Taxonomy. Evaluation results confirm the effectiveness of the framework. Findings from the evaluation also offer valuable insights to decision makers who develop and improve data standards, select and adopt data standards, or consume standards-based data.