¹Telematics Engineering Group, Universidad del Cauca, Campus Tulcán, Popayán, Colombia.
²Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911, Leganés, Spain.

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Abstract—Large Volume of Data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through data mining and data science methodologies. Nevertheless these not tackle the issues in data quality clearly, leaving out relevant activities. We proposed a conceptual framework for data quality in knowledge discovery tasks based on CRISP-DM, SEMMA and Data Science, considering the issues of ESE Taxonomy.

Index Terms—CRISP-DM, data quality framework, data science, ESE taxonomy, FDQ-KDT, Knowledge discovery, SEMMA.

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Cite: David Camilo Corrales, Agapito Ledezma, Juan Carlos Corrales, "A Conceptual Framework for Data Quality in Knowledge Discovery Tasks (FDQ-KDT): A Proposal," Journal of Computers vol. 10, no. 6, pp. 396-405, 2015.