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.


  • Dr Karl Presser
  • David Weber
  • Projects

  • TDS-Exposure
  • Publications