Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Quality views: capturing and exploiting the user perspective on data quality

Missier, Paolo, Embury, Suzanne, Greenwood, Mark, Preece, Alun David and Jin, Binling 2006. Quality views: capturing and exploiting the user perspective on data quality. Presented at: 32nd International Conference on Very Large Data Bases, Seoul, Korea, 12-15 September 2006. Published in: Dayal, Umeshwar ed. VLDB '06: Proceedings of the 32nd International Conference on Very Large Databases. NewYork, NY: Association for Computing Machinery, pp. 977-988.

Full text not available from this repository.

Abstract

There is a growing awareness among life scientists of the variability in quality of the data in public repositories, and of the threat that poor data quality poses to the validity of experimental results. No standards are available, however, for computing quality levels in this data domain. We argue that data processing environments used by life scientists should feature facilities for expressing and applying quality-based, personal data acceptability criteria.We propose a framework for the specification of users' quality processing requirements, called quality views. These views are compiled and semi-automatically embedded within the data processing environment. The result is a quality management toolkit that promotes rapid prototyping and reuse of quality components. We illustrate the utility of the framework by showing how it can be deployed within Taverna, a scientific workflow management tool, and applied to actual workflows for data analysis in proteomics.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Association for Computing Machinery
ISBN: 9781595933850
Related URLs:
Last Modified: 15 Sep 2017 05:57
URI: http://orca-mwe.cf.ac.uk/id/eprint/46352

Citation Data

Cited 56 times in Google Scholar. View in Google Scholar

Actions (repository staff only)

Edit Item Edit Item