Models for Distributed, Large Scale Data Cleaning

Poor data quality is a serious and costly problem affecting organizations across all industries. Real data is often dirty, containing missing, erroneous, incomplete, and duplicate values. Declarative data cleaning techniques have been proposed to resolve some of these underlying errors by identifying the inconsistencies and proposing updates to the data. However, much of this work has focused on cleaning data in static environments. Given the …