Abstract
We describe a new approach to managing information quality (IQ) in an e-Science context, by allowing scientists to define the quality characteristics that are of importance in their particular domain. These preferences are specified and classified in relation to a formal IQ ontology, intended to support the discovery and reuse of scientists’ quality descriptors and metrics. In this paper, we present a motivating scenario from the biological sub-domain of proteomics, and use it to illustrate how the generic quality model we have developed can be expanded incrementally without making unreasonable demands on the domain expert who maintains it.
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References
Aebersold, R., Mann, M.: Mass spectrometry-based proteomics. Nature 422, 198–207 (2003)
Baader, F., Horrocks, I., Sattler, U.: Description Logics. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 3–28. Springer, Heidelberg (2004)
English, L.: Improving Data Warehouse and Business Information Quality. Wiley, Chichester (1999)
Brazma, A., et al.: Minimum Information about a Microarray Experiment (MIAME) — Toward Standards for Microarray Data. Nature Genetics 29, 365–371 (2001)
Missier, P., Embury, S., Greenwood, M., Preece, A., Jin, B.: An ontology-based approach to handling information quality in e-science. In: Proc 4th e-Science All Hands Meeting (2005)
Pandey, A., Mann, M.: Proteomics to study genes and genomes. Nature 405, 837–846 (2000)
Patterson, S.D., Aebersold, R.H.: Proteomics: the first decade and beyond. Nature Genetics 33(Supplement), 311–323 (2003)
Redman, T.C.: Data Quality for the Information Age. Artech House (1996)
Wang, R.Y., Ziad, M., Lee, Y.W.: Data Quality. Advances in Database Systems. Kluwer Academic Publishers, Dordrecht (2001)
Wand, Y., Wang, R.: Anchoring data quality dimensions in ontological foundations. Communications of the ACMÂ 39(11) (1996)
Wang, R.Y., Strong, D.M.: Beyond accuracy: What data quality means to data consumers. Journal of Management Information System 12(4) (1996)
Wroe, C., Stevens, R., Goble, C., Roberts, A., Greenwood, M.: A suite of DAML+OIL ontologies to describe bioinformatics web services and data. International Journal of Cooperative Information Systems 12(2), 197–224 (2003)
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Missier, P. et al. (2005). Managing Information Quality in e-Science: A Case Study in Proteomics. In: Akoka, J., et al. Perspectives in Conceptual Modeling. ER 2005. Lecture Notes in Computer Science, vol 3770. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11568346_45
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DOI: https://doi.org/10.1007/11568346_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29395-8
Online ISBN: 978-3-540-32239-9
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