Scalable machine learning for data cleaning

Description

Machine learning tools promise to help solve data curation problems. While the principles are well understood, the engineering details in configuring and deploying ML techniques are the biggest hurdle. Ihab Ilyas explains why leveraging data semantics and domain-specific knowledge is key in delivering the optimizations necessary for truly scalable ML curation solutions.

Ihab focuses on two main problems: entity consolidation, which is arguably the most difficult data curation challenge because it is notoriously complex and hard to scale, and using probabilistic inference to enrich data and suggest data repair for identified errors and anomalies. The problem statement in both cases sounds deceptively simple: find all the records from a collection of multiple data sources that refer to the same real-world entity or use trusted data sources to suggest how to correct errors. However, both problems have been challenging researchers and practitioners for decades due to the fundamentally combinatorial explosion in the space of solutions and the lack of ground truth.

There’s a large body of work on this problem by both academia and industry. Techniques have included human curation, rules-based systems, and automatic discovery of clusters using predefined thresholds on record similarity Unfortunately, none of these techniques alone has been able to provide sufficient accuracy and scalability. Ihab provides deeper insight into the entity consolidation and data repair problems and discusses how machine learning, human expertise, and problem semantics collectively can deliver a scalable, high-accuracy solution.