There are millions of searchable data sources on the Web and to a large extent their contents can only be reached through their own query interfaces. There is an enormous interest in making the data in these sources easily accessible. There are primarily two general approaches to achieve this objective. The first is to surface the contents of these sources from the deep Web and add the contents to the index of regular search engines. The second is to integrate the searching capabilities of these sources and support integrated access to them. In this book, we introduce the state-of-the-art techniques for extracting, understanding, and integrating the query interfaces of deep Web data sources. These techniques are critical for producing an integrated query interface for each domain. The interface serves as the mediator for searching all data sources in the concerned domain. While query interface integration is only relevant for the deep Web integration approach, the extraction and understanding of query interfaces are critical for both deep Web exploration approaches. This book aims to provide in-depth and comprehensive coverage of the key technologies needed to create high quality integrated query interfaces automatically. The following technical issues are discussed in detail in this book: query interface modeling, query interface extraction, query interface clustering, query interface matching, query interface attribute integration, and query interface integration. Table of Contents: Introduction / Query Interface Representation and Extraction / Query Interface Clustering and Categorization / Query Interface Matching / Query Interface Attribute Integration / Query Interface Integration / Summary and Future Research

Among the search tools currently on the Web, search engines are the most well known thanks to the popularity of major search engines such as Google and Yahoo!. While extremely successful, these major search engines do have serious limitations. This book introduces large-scale metasearch engine technology, which has the potential to overcome the limitations of the major search engines. Essentially, a metasearch engine is a search system that supports unified access to multiple existing search engines by passing the queries it receives to its component search engines and aggregating the returned results into a single ranked list. A large-scale metasearch engine has thousands or more component search engines. While metasearch engines were initially motivated by their ability to combine the search coverage of multiple search engines, there are also other benefits such as the potential to obtain better and fresher results and to reach the Deep Web. The following major components of large-scale metasearch engines will be discussed in detail in this book: search engine selection, search engine incorporation, and result merging. Highly scalable and automated solutions for these components are emphasized. The authors make a strong case for the viability of the large-scale metasearch engine technology as a competitive technology for Web search. Table of Contents: Introduction / Metasearch Engine Architecture / Search Engine Selection / Search Engine Incorporation / Result Merging / Summary and Future Research