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- W3074100554 abstract "Data Quality Management (DQM) concerns a wide range of tasks and techniques, largely used by companies and organizations for assessing and improving the quality of their data. Data Quality (DQ) is defined as fitness for use and naturally depends on application context and usage needs. Moreover, context is embedded in DQM tasks, for example, in the definition of DQ metrics, in the discovery of DQ rules or in the elicitation of DQ requirements. However, despite its recognized importance for DQM, the literature only manages obvious contextual aspects of data, and lacks of proposals for context definition, specification and usage within major DQM tasks. This PhD thesis is at the junction of these two main topics: Data Quality and Context. Our objective is to model context for DQM, exploiting the contextual nature of data, at each phase of the DQM process. We aim to provide a general model of context for DQM, an approach for using the model within a DQM project, and a proof of concept in the domain of Digital Government." @default.
- W3074100554 created "2020-08-24" @default.
- W3074100554 creator A5007924105 @default.
- W3074100554 date "2020-01-01" @default.
- W3074100554 modified "2023-10-14" @default.
- W3074100554 title "Handling Context in Data Quality Management" @default.
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- W3074100554 doi "https://doi.org/10.1007/978-3-030-55814-7_32" @default.
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