[Paper Review] Guided Data Repair

Mohamed Yakout, Ahmed K. Elmagarmid+3 more|arXiv (Cornell University)|Mar 16, 2011

Data Quality and Management18 references4 citations

TL;DR

This paper proposes GDR, a Guided Data Repair framework that integrates user feedback and machine learning to enhance automatic data cleaning with minimal user involvement. By using value of information (VOI) to prioritize user consultations and active learning to rank repairs, GDR significantly improves data quality while reducing user effort, as validated on a real-world dataset.

ABSTRACT

In this paper we present GDR, a Guided Data Repair framework that incorporates user feedback in the cleaning process to enhance and accelerate existing automatic repair techniques while minimizing user involvement. GDR consults the user on the updates that are most likely to be beneficial in improving data quality. GDR also uses machine learning methods to identify and apply the correct updates directly to the database without the actual involvement of the user on these specific updates. To rank potential updates for consultation by the user, we first group these repairs and quantify the utility of each group using the decision-theory concept of value of information (VOI). We then apply active learning to order updates within a group based on their ability to improve the learned model. User feedback is used to repair the database and to adaptively refine the training set for the model. We empirically evaluate GDR on a real-world dataset and show significant improvement in data quality using our user guided repairing process. We also, assess the trade-off between the user efforts and the resulting data quality.

Motivation & Objective

• To reduce user involvement in data repair while maintaining or improving data quality.
• To develop a framework that intelligently selects which data repairs to consult users about, based on expected impact.
• To use machine learning to automatically apply high-utility repairs without user input.
• To dynamically refine the model using user feedback, improving future repair recommendations.
• To evaluate the trade-off between user effort and data quality improvement in real-world scenarios.

Proposed method

• GDR groups potential data repairs and quantifies each group's utility using the value of information (VOI) from decision theory.
• Within each group, active learning ranks individual repairs based on their expected contribution to improving the predictive model.
• Users are consulted only on the highest-utility repairs identified by VOI and active learning, minimizing interaction.
• The system applies machine learning to automatically apply repairs with high predicted utility, avoiding user involvement for these updates.
• User feedback is used both to repair the database and to retrain and refine the machine learning model for future recommendations.
• The framework is evaluated on a real-world dataset to assess data quality improvement and user effort trade-offs.

Experimental results

Research questions

• RQ1 How can user feedback be strategically guided to maximize data quality improvement with minimal user effort?
• RQ2 What is the optimal way to rank potential data repairs for user consultation to improve repair efficiency?
• RQ3 How does integrating machine learning with user feedback affect the scalability and accuracy of data repair?
• RQ4 What is the trade-off between user effort and data quality improvement in a real-world setting?
• RQ5 Can adaptive model refinement using user feedback lead to sustained improvements in repair quality over time?

Key findings

• GDR significantly improves data quality by focusing user feedback on the most impactful repairs, as determined by value of information (VOI) analysis.
• The use of active learning within repair groups reduces the number of user consultations needed while maintaining high repair accuracy.
• Machine learning components successfully apply a substantial portion of repairs automatically, minimizing user involvement.
• User feedback effectively improves the model’s ability to predict high-utility repairs, leading to better future recommendations.
• The framework achieves a favorable trade-off between user effort and data quality, with measurable gains in data quality metrics on the real-world dataset.
• Empirical evaluation confirms that GDR outperforms baseline automatic repair methods in data quality improvement while requiring less user input.