Accurate Schema Matching on Streams

We address the problem of matching imperfectly documented schemas of data streams and large databases. Instance-level schema matching algorithms identify likely correspondences between attributes by quantifying the similarity of their corresponding values. However, exact calculation of these similarities requires processing of all database records—which is infeasible for data streams. We devise a fast matching algorithm that uses only a small sample of records, and is yet guaranteed to match the most similar attributes with high probability. The method can be applied to any given (combination of) similarity metrics that can be estimated from a sample with bounded error; we apply the algorithm to several metrics. We give a rigorous proof of the method’s correctness and report on experiments using large databases.