Flexible string matching against large databases in practice. Nick Koudas, Amit Marathe and Divesh Srivastava. Data Cleaning is an important process that has been at the center of research interest in recent years. Poor data quality is the result of a variety of reasons, including data entry errors and multiple conventions for recording database fields, and has a significant impact on a variety of business issues. Hence, there is a pressing need for technologies that enable flexible (fuzzy) matching of string information in a database. Cosine similarity with tf-idf is a well-established metric for comparing text, and recent proposals have adapted this similarity measure for flexibly matching a query string with values in a single attribute of a relation. In deploying tf-idf based flexible string matching against real AT&T databases, we observed that this technique needed to be enhanced in many ways. First, along the functionality dimension, where there was a need to flexibly match along multiple string-valued attributes, and also take advantage of known semantic equivalences. Second, we identified various performance enhancements to speed up the matching process, potentially trading off a small degree of accuracy for substantial performance gains. In this paper, we report on our techniques and experience in dealing with flexible string matching against real AT&T databases.