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Managing data quality of integrated data with known provenance

Managing data quality of integrated data with known provenance

Del Pilar Angeles, Maria and MacKinnon, Lachlan M. (2011) Managing data quality of integrated data with known provenance. International Journal of Information Quality, 2 (3). pp. 244-263. ISSN 1751-0457 (Print), 1751-0465 (Online) (doi:https://doi.org/10.1504/IJIQ.2011.040671)

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Abstract

Users querying a database system will have returned to them a set of data with no indication of the qualitative value of that data. In order to address the issue of data quality, and challenging the presumptions of perfection, atomicity and primary authorship, a toolset has been developed. This project proposes a data quality manager (DQM), which contains a reference model, a measurement model along with an assessment model. The present work aims to identify data quality criteria to measure and assess data quality of derived data, as well as data at multiple levels of granularity. The qualitative information provided by the DQM is enhanced by considering data provenance. The qualitative measures allow the ranking of data sources based on users' specification of the context in a heterogeneous multi database environment. The DQM prototype has been tested and several experiments have been carried out in order to prove that more accurate information is being provided to the users.

Item Type: Article
Additional Information: [1] First published: 2011. [2] Published as: International Journal of Information Quality, (2011), Vol. 2, (3), pp. 244-263.
Uncontrolled Keywords: data quality, data provenance, heterogeneous database systems, information quality, quality management, granularity, data retrieval, information retrieval
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Pre-2014 Departments: School of Computing & Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:19
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/7512

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