Merging the Results of Approximate Match Operations Sudipto Guha, Nick Koudas, Amit Marathe, and Divesh Srivastava   View Paper (PDF)   Return to RESEARCH SESSION 17:TOP-K RANKING Abstract Data Cleaning is an important process that has been at the center of research interest in recent years. An important end goal of effective data cleaning is to identify the relational tuple or tuples that are "most related" to a given query tuple. Various techniques have been proposed in the literature for efficiently identifying approximate matches to a query string against a single attribute of a relation. In addition to constructing a ranking (i.e., ordering) of these matches, the techniques often associate, with each match, scores that quantify the extent of the match. Since multiple attributes could exist in the query tuple, issuing approximate match operations for each of them separately will effectively create a number of ranked lists of the relation tuples. Merging these lists to identify a final ranking and scoring, and returning the top-K tuples, is a challenging task. In this paper, we adapt the well-known foot rule distance (for merging ranked lists) to effectively deal with scores. We study efficient algorithms to merge rankings, and produce the top-K tuples, in a declarative way. Since techniques for approximately matching a query string against a single attribute in a relation are typically best deployed in a database, we introduce and describe two novel algorithms for this problem and we provide SQL specifications for them. Our experimental case study, using real application data along with a realization of our proposed techniques on a commercial data base system, highlights the benefits of the proposed algorithms and attests to the overall effectiveness and practicality of our approach. ©2005 Association for Computing Machinery