Collaborative Data Cleaning Framework: a Pilot Case Study for Machine Learning Development

Abstract

This study experiments with collaborative data cleaning, a pivotal phase in data preparation for both analysis and machine learning. We used a provenance Data Cleaning Model (DCM) for multi-user scenarios to track changes on a dataset and conduct comprehensive experiments that simulate multiple data curators working collaboratively on a dataset. Furthermore, we analyzed how different data-cleaning scenarios to improve quality metrics of completeness and correctness of a dataset can affect the downstream machine learning modeling performance.