A Conceptual Framework for Data Quality in Knowledge Discovery Tasks (FDQ-KDT): A Proposal Articles uri icon

publication date

  • November 2015

start page

  • 396

end page

  • 405

issue

  • 6

volume

  • 10

international standard serial number (ISSN)

  • 1796-203X

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