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A data driven learning approach for the assessment of data quality.

Erik Tute1, Nagarajan Ganapathy2, Antje Wulff2

  • 1Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany. Erik.Tute@plri.de.

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PubMed
Summary

A new data-driven approach learns task-specific rules for data quality assessment, identifying suitable measurement methods and thresholds. This method effectively complements existing techniques for determining dataset suitability for various tasks.

Keywords:
Data aggregationData qualityInformation scienceKnowledge basesMachine learning

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Area of Science:

  • Data Science
  • Machine Learning
  • Data Quality Management

Background:

  • Data quality assessment is crucial but complex and task-dependent.
  • Identifying appropriate measurement methods and reference ranges for data quality is challenging.
  • Current manual and data-driven methods have limitations in learning task-dependent quality thresholds.

Purpose of the Study:

  • To explore a data-driven approach for learning task-dependent knowledge on data quality assessment.
  • To identify suitable measurement methods and assessment criteria for data quality.
  • To provide knowledge for determining data stock suitability for specific tasks.

Main Methods:

  • Generated artificial data with predefined quality issues.
  • Applied generic measurement methods to the data.
  • Trained decision trees on measurement results and data suitability outcomes.
  • Derived and evaluated rules from decision trees against known data quality issues.

Main Results:

  • Decision trees identified rules for 12 out of 19 defined data quality issues.
  • Learned knowledge complemented manual interpretation of measurement results.
  • The approach demonstrated effectiveness in suggesting rules for data quality characteristics.

Conclusions:

  • The data-driven approach generates valuable task-dependent knowledge for data quality assessment.
  • This method effectively complements existing data quality assessment approaches.
  • The approach successfully suggested rules for assessing dataset suitability for specific tasks.