Proof positive and negative in data cleaning

One notoriously hard data cleaning problem is, given a database, how to precisely capture which value is correct (i.e., proof positive) or wrong (i.e., proof negative). Although integrity constraints have been widely studied to capture data errors as violations, the accuracy of data cleaning using integrity constraints has long been controversial. Overall they deem one fundamental problem: Given a set of data values that together forms a violation, there is no evidence of which value is proof positive or negative. Hence, it is known that integrity constraints themselves cannot guide dependable data cleaning. In this work, we introduce an automated method for proof positive and negative in data cleaning, based on Sherlock rules and reference tables. Given a tuple and reference tables, Sherlock rules tell us what attributes are proof positive, what attributes are proof negative and (possibly) how to update them. We study several fundamental problems associated with Sherlock rules. We also present efficient algorithms for cleaning data using Sherlock rules. We experimentally demonstrate that our techniques can not only annotate data with proof positive and negative, but also repair data when enough information is available.