Improving Uncertain Data-Quality through Effective Use of Knowledge Base

Huang, Zhimao; Peng, Hong

doi:10.1109/wicom.2008.2517

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…On the other hand, it is important to indicate that this work does not focus on data sources and ETL layers quality issues, since many researches have already addressed them. For example, the works in [19][21] [27] focus on data quality in DW sources, and the works in [21][44] [63] focus on quality, especially data cleaning, in ETL processes. In particular, the works in [30][64] focus on the contexts of the ETL processes and the data sources, respectively.…”

Section: Dq Assessment In Dws Based On Contextsmentioning

confidence: 99%

Context-based Data Quality Metrics in Data Warehouse Systems

Serra¹,

Marotta²

2017

CLEIej

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The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in research and industry. DQ in this kind of systems becomes essential. This work presents a proposal for identifying DQ problems in the domain of DWS, considering the different contexts that exist in each system component. This proposal may act as a first conceptual framework that guides the DQ-responsible in the management of DQ in DWS. The main contributions of this work are a thorough literature review about how contexts are used for evaluating DQ in DWS, and a proposal for assessing DQ in DWS through contextbased DQ metrics.

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