Researchers and practitioners have proposed conceptual frameworks that detail the different factors determining the quality of data. Yet, methods and tools for managing data quality in information systems still tend to deal only with basic data quality issues of syntactic correctness, completeness and consistency supported in classic constraint models and mechanisms.
We are investigating how better support for measuring and improving data quality can be integrated into information systems. Our investigations focus on scientific information systems where the empirical data can form the basis for formulating scientific theories and making policy decisions. Factors that may affect the quality of data include the reliability of the methods used to measure the values and this may evolve over time as new instruments and techniques are introduced. In a series of projects, we have been working closely together with food scientists to develop the FoodCASE system to manage information about the composition of foods in terms of both nutrients and contaminants. This has enabled us to examine in detail what data quality means to them while learning about their requirements and work practices.
Our research is addressing the data quality problem in two ways. First, we are exploring metrics that can be used to measure data quality and identify specific data quality issues within a database. Second, we are investigating how traditional constraint models and mechanisms can be adapted and extended to deal with a wider range of data quality requirements.
- An Assessment Impact Classification of Data Quality Requirements in Food Composition Database Systems By Karl Presser, David Weber, Moira C. Norrie in Proc. 2nd IMEKOFOODS, Benevento, Italy, October 2016
- FoodCASE: A System to Manage Food Composition, Consumption and TDS Data By Karl Presser, David Weber, Moira C. Norrie in Food Chemistry
- UnifiedOCL: Achieving System-Wide Constraint Representations By David Weber, Jakub Szymanek, Moira C. Norrie in Proc. 35th Intl. Conf. on Conceptual Modeling (ER 2016), Gifu, Japan, November 2016 PDF
- A Scope Classification of Data Quality Requirements for Food Composition Data By Karl Presser, Hans Hinterberger, David Weber, Moira C. Norrie in Food Chemistry PDF
- How to Give Feedback on Data Quality: A Study in the Food Sciences By David Weber, Karl Presser, Moira C. Norrie in Proc. 23rd European Conference on Information Systems (ECIS), Munster, Germany, May 2015 PDF
- A Study of Data Quality Requirements for Empirical Data in the Food Sciences By Karl Presser, David Weber, Moira C. Norrie in Proc. 22nd European Conference on Information Systems (ECIS), Tel Aviv, Israel, June 2014 PDF
- Towards Harmonized Data Interchange in Food Consumption Data By Heikki Pakkala, Tue Christensen, Karl Presser, Ignazio Martinez de Victoria in Computer Standards & Interfaces, Vol. 36, No. 3, pp 592-597 PDF