  Many applications need to clean data with a target accuracy. As far as we
know, this problem has not been studied in depth. In this paper we take the
first step toward solving it. We focus on value normalization (VN), the problem
of replacing all string that refer to the same entity with a unique string. VN
is ubiquitous, and we often want to do VN with 100% accuracy. This is typically
done today in industry by automatically clustering the strings then asking a
user to verify and clean the clusters, until reaching 100% accuracy. This
solution has significant limitations. It does not tell the users how to verify
and clean the clusters. This part also often takes a lot of time, e.g., days.
Further, there is no effective way for multiple users to collaboratively verify
and clean. In this paper we address these challenges. Overall, our work
advances the state of the art in data cleaning by introducing a novel cleaning
problem and describing a promising solution template.
