Human-Centric Data Cleaning [Vision]

TL;DR

This paper envisions a comprehensive human-centric data cleaning framework that systematically integrates human input throughout the entire cleaning process, addressing current gaps in algorithm-driven approaches.

Contribution

It proposes a design vision for an end-to-end data cleaning framework that involves humans at all stages, beyond existing algorithm-centric methods.

Findings

Identifies key challenges in developing human-in-the-loop data cleaning

Motivates the need for a systematic framework involving humans

Suggests directions for implementing such a framework

Abstract

Data Cleaning refers to the process of detecting and fixing errors in the data. Human involvement is instrumental at several stages of this process, e.g., to identify and repair errors, to validate computed repairs, etc. There is currently a plethora of data cleaning algorithms addressing a wide range of data errors (e.g., detecting duplicates, violations of integrity constraints, missing values, etc.). Many of these algorithms involve a human in the loop, however, this latter is usually coupled to the underlying cleaning algorithms. There is currently no end-to-end data cleaning framework that systematically involves humans in the cleaning pipeline regardless of the underlying cleaning algorithms. In this paper, we highlight key challenges that need to be addressed to realize such a framework. We present a design vision and discuss scenarios that motivate the need for such a framework to judiciously assist humans in the cleaning process. Finally, we present directions to implement such a framework.