Query optimization in information integration

The problem of decentralized data sharing, which is relevant to a wide range of applications, is still a source of major theoretical and practical challenges, in spite of many years of sustained research. In this paper we focus on the challenge of efficiency of query evaluation in information integration systems that use the global-as-view approach, with the objective of developing query-processing strategies that would be widely applicable and easy to implement in real-life applications. Our algorithms take into account important features of today’s data sharing applications: XML as likely interface or representation for data sources; the potential for information overlap across data sources; and the need for inter-source processing, as in joins of data across sources. The focus of this paper is on performance-related characteristics of several alternative approaches that we propose for efficient query processing in information integration, including an approach that uses materialized restructured views. We use synthetic and real-life datasets in our implementation of an information integration system shell to provide experimental results that demonstrate that our algorithms are efficient and competitive in the information integration setting. In addition, our experimental results allow us to make context-specific recommendations on selecting query-processing approaches from our proposed alternatives. As such, our approaches could form a basis for scalable query processing in information integration and interoperability in many practical settings.