Measuring and Comparing Effectiveness of Data Quality Techniques

Authors:
Lei Jiang;Daniele Barone;Alex Borgida;John Mylopoulos
Affiliations:
Dept. of Computer Science, University of Toronto,;Dept. of Computer Science, Università di Milano Bicocca,;Dept. of Computer Science, University of Toronto, and Dept. of Computer Science, Rutgers University,;Dept. of Computer Science, University of Toronto, and Dept. of Information Engineering and Computer Science, University of Trento,
Venue:
CAiSE '09 Proceedings of the 21st International Conference on Advanced Information Systems Engineering
Year:
2009

Citing 13
Cited 0

Conceptual database design: an Entity-relationship approach

Conceptual database design: an Entity-relationship approach
Modeling Information Manufacturing Systems to Determine Information Product Quality

Management Science
Data quality assessment

Communications of the ACM - Supporting community and building social capital
Information Retrieval

Information Retrieval
Information and Database Quality

Information and Database Quality
Software Metrics: A Rigorous and Practical Approach

Software Metrics: A Rigorous and Practical Approach
Table Oriented Metrics for Relational Databases

Software Quality Control
Modeling Completeness versus Consistency Tradeoffs in Information Decision Contexts

IEEE Transactions on Knowledge and Data Engineering
Using Checksums to Detect Data Corruption

EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Metrics for Evaluating the Quality of Entity Relationship Models

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)
Object-Relational Database Metrics Formalization

QSIC '06 Proceedings of the Sixth International Conference on Quality Software
Conditional functional dependencies for capturing data inconsistencies

ACM Transactions on Database Systems (TODS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Poor quality data may be detected and corrected by performing various quality assurance activities that rely on techniques with different efficacy and cost. In this paper, we propose a quantitative approach for measuring and comparing the effectiveness of these data quality (DQ) techniques. Our definitions of effectiveness are inspired by measures proposed in Information Retrieval. We show how the effectiveness of a DQ technique can be mathematically estimated in general cases, using formal techniques that are based on probabilistic assumptions. We then show how the resulting effectiveness formulas can be used to evaluate, compare and make choices involving DQ techniques.