Data integration systems often provide a uniform query interface, called amediated schema, to a multitude of data sources. To answer user queries, such systems employ a set ofsemantic matchesbetween 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 ofimproving the mediated schema, to make finding such matches easier. Specifically, a mediated schemaSwill 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, calledmSeer. Given a mediated schemaSand a matching toolM,mSeerfirst computes a matchability score that quantifies how wellScan be matched against usingM. Next,mSeeruses this score to generate a matchability report that identifies the problems in matchingS.Finally,mSeeraddresses these problems by automatically suggesting changes toS(e.g., renaming an attribute, reformatting data values, etc.) that it believes will preserve the semantics ofSand yet make it more amenable to matching. We present extensive experiments over several real-world domains that demonstrate the promise of the proposed approach.

Keyword search is a familiar and potentially effective way to find information of interest that is "locked" inside relational databases. Current work has generally assumed that answers for a keyword query reside within a single database. Many practical settings, however, require that we combine tuples from multiple databases to obtain the desired answers. Such databases are often autonomous and heterogeneous in their schemas and data. This paper describes Kite, a solution to the keyword-search problem over heterogeneous relational databases. Kite combines schema matching and structure discovery techniques to find approximate foreign-key joins across heterogeneous databases. Such joins are critical for producing query results that span multiple databases and relations. Kite then exploits the joins - discovered automatically across the databases - to enable fast and effective querying over the distributed data. Our extensive experiments over real-world data sets show that (1) our query processing algorithms are efficient and (2) our approach manages to produce high-quality query results spanning multiple heterogeneous databases, with no need for human reconciliation of the different databases.

We consider the problem of estimating occurrence rates of rare eventsfor extremely sparse data, using pre-existing hierarchies to perform inference at multiple resolutions. In particular, we focus on the problem of estimating click rates for (webpage, advertisement) pairs (called impressions) where both the pages and the ads are classified into hierarchies that capture broad contextual information at different levels of granularity. Typically the click rates are low and the coverage of the hierarchies is sparse. To overcome these difficulties we devise a sampling method whereby we analyze aspecially chosen sample of pages in the training set, and then estimate click rates using a two-stage model. The first stage imputes the number of (webpage, ad) pairs at all resolutions of the hierarchy to adjust for the sampling bias. The second stage estimates clickrates at all resolutions after incorporating correlations among sibling nodes through a tree-structured Markov model. Both models are scalable and suited to large scale data mining applications. On a real-world dataset consisting of 1/2 billion impressions, we demonstrate that even with 95% negative (non-clicked) events in the training set, our method can effectively discriminate extremely rare events in terms of their click propensity.