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Information integration has been a subject of research for several decades, and despite significant progress, in particular in recent years, it still remains a very active research area. The volume of data available online and in electronic form has grown exponentially over the recent decade, increasing the significance of information integration. Many important applications, such as large-scale research in life sciences, cooperation and coordination among government agencies, and business intelligence require or benefit from information integration. In this paper we focus on the issue of query processing efficiency in the Semantic Model approach to information integration. The Semantic Model approach was proposed recently and encompasses a number of advantages over other techniques including reduced effort and time for schema mediation, ability to incorporate various types of sources including relational and XML, supporting gradual integration, and opportunity for additional query optimization. We report our implementation of the Local-as-View mappings in the Semantic Model approach, and present query processing and optimization algorithms. Experimental studies of the performance of the algorithms are also presented.