Abstract
Data quality management (DQM) is a complex task involving activities for data quality (DQ) assessment and improvement. Many DQ methodologies address DQM (sometimes partially), and are made up of several stages, where many DQM activities are carried out. According to the literature, most of these activities are influenced by the context of data. However, very few state-of-the-art DQ methodologies consider the context of data, and when they do, context is addressed only at few stages. In this work, we propose a context-aware data quality management (CaDQM) methodology, that clarifies the influence of context in most DQM activities. In particular, context components are identified at early stages and are used at all stages of the CaDQM.
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Notes
- 1.
Research project developed by the e-Government and Information and Knowledge Society Agency (AGESIC) of Uruguay: https://www.gub.uy/agencia-gobierno-electronico-sociedad-informacion-conocimiento/.
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Serra, F., Peralta, V., Marotta, A., Marcel, P. (2023). Context-Aware Data Quality Management Methodology. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_22
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