Data integration systems often provide a uniform query interface, called a mediated schema, to a multitude of data sources. To answer user queries, such systems employ a set of semantic matches between the mediated schema and the data-source schemas. Finding such matches is well known to be difficult. Hence much work has focused on developing semi-automatic techniques to efficiently find the matches. In this paper we consider the complementary problem of improving the mediated schem,a, to make finding such matches easier. Specifically, a mediated schema S will typically be matched with many source schemas. Thus, can the developer of S analyze and revise S in a way that preserves S 's semantics, and yet makes it easier to match with in the future?In this paper we provide an affirmative answer to the above question, and outline a promising solution direction, called mSeer. Given a mediated schema S and a matching tool M, mSeer first computes a matchability score that quantifies how well S can be matched against using M. Next, mSeer uses this score to generate a matchability report that identifies the problems in matching S. Finally, mSeer addresses these problems by automatically suggesting changes to S (e.g., renaming an attribute, reformatting data values, etc.) that it believes will preserve the semantics of S and yet make it more amenable to matching. We present extensive experiments over several real-world domains that demonstrate the promise of the proposed approach.
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