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Data Quality Mining - Making a Virtue of Necessity - Cornell University - cs cornell

Data Quality Mining - Making a Virtue of Necessity - Cornell University - cs cornell 2025

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The document introduces Data Quality Mining (DQM) as a novel approach that utilizes data mining techniques to identify, quantify, explain, and rectify data quality issues in large databases. It emphasizes the importance of data quality in Knowledge Discovery in Databases (KDD) and suggests that improving data quality can enhance KDD outcomes while also serving as an independent goal. The paper outlines how association rules can be applied within DQM to assess and improve data quality, highlighting the need for systematic approaches to address deficiencies commonly found in practical applications.

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Click ‘Get Form’ to open it in the editor.
Begin by reviewing the abstract section, which outlines the purpose of data quality mining (DQM). Familiarize yourself with key concepts as they will guide your understanding of the form.
Proceed to fill out your contact information in the designated fields. Ensure accuracy as this information is crucial for follow-up communications.
In the motivation section, reflect on how DQM can impact your work. Provide insights or examples that relate to your experience with data quality issues.
For the data quality mining section, detail any specific methodologies you plan to employ. Use our platform's tools to annotate or highlight important points.
Finally, review all entries for completeness and clarity before submitting. Utilize our platform’s features to save and share your document easily.

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Data Quality Mining - Making a Virtue of Necessity - Cornell University - cs cornell 2025

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Data Quality Mining - Making a Virtue of Necessity - Cornell University - cs cornell 2025

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