TOWARD THE SCALABLE INTEGRATION OF INTERNET INFORMATION SOURCES

This dissertation in a broad sense focuses on understanding the fundamental aspects of building a large-scale information integration system that can answer complex queries over a large number of heterogeneous Internet data sources. Among many challenges in achieving this goal, we focus on two key issues: efficient query processing and schema matching. Most of the data the integration system processes arrives in a stream from a remote source rather than residing on a local disk; we need to develop efficient query processing algorithms that work in this environment. We specifically investigate algorithms for evaluating sliding window joins over pairs of unbounded streams. We introduce a unit-time-basis cost model to analyze the expected performance of these algorithms. Using this cost model, we propose strategies for maximizing the efficiency of processing joins over unbounded streams. With respect to schema matching, we introduce an automated technique that works in the particularly difficult cases in which attribute names and data values are “opaque. ” While schema matching has always been a problematic and interesting aspect of information integration, the problem is exacerbated as the number of information sources to be integrated grows. Ou