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• W2960727496 abstract "High-quality is key to interpretable and trustworthy analytics and the basis for meaningful data-driven decisions. In practical scenarios, quality is typically associated with preprocessing, profiling, and cleansing for subsequent tasks like integration or analytics. However, from a scientific perspective, a lot of research has been published about the measurement (i.e., the detection) of quality issues and different generally applicable quality dimensions and metrics have been discussed. In this work, we close the gap between research into quality measurement and practical implementations by investigating the functional scope of current quality tools. With a systematic search, we identified 667 software tools dedicated to data quality, from which we evaluated 13 tools with respect to three functionality areas: (1) profiling, (2) quality measurement in terms of metrics, and (3) continuous quality monitoring. We selected the evaluated tools with regard to pre-defined exclusion criteria to ensure that they are domain-independent, provide the investigated functions, and are evaluable freely or as trial. This survey aims at a comprehensive overview on state-of-the-art quality tools and reveals potential for their functional enhancement. Additionally, the results allow a critical discussion on concepts, which are widely accepted in research, but hardly implemented in any tool observed, for example, generally applicable quality metrics." @default.
• W2960727496 created "2019-07-23" @default.
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• W2960727496 date "2019-07-18" @default.
• W2960727496 modified "2023-09-27" @default.
• W2960727496 title "A Survey of Data Quality Measurement and Monitoring Tools." @default.
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• W2960727496 hasPublicationYear "2019" @default.
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