A highly scalable and effective method for metasearch
ACM Transactions on Information Systems (TOIS)
Building efficient and effective metasearch engines
ACM Computing Surveys (CSUR)
Supporting metasearch with XSL
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
A generic alerting service for digital libraries
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Distributed top-N query processing with possibly uncooperative local systems
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A conceptual model for user-centered quality information retrieval on the World Wide Web
Journal of Intelligent Information Systems
Querying e-catalogs using content summaries
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
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Modern digital libraries require user-friendly and yet responsive access to the rapidly growing, heterogeneous, and distributed collection of information sources. The increasing volume and diversity of digital information available online have led to a growing problem that conventional data management systems do not have, namely finding which information sources out of many candidate choices are the most relevant to answer a given user query. We refer to this problem as the query routing problem. In this paper we introduce the notation and issues of query routing, and present a practical solution for designing a scalable query routing system based on multi-level progressive pruning strategies. The key idea is to create and maintain user query profiles and source capability profiles independently, and to provide algorithms that can dynamically discover relevant information sources for a given query through the smart use of user query profiles and source capability profiles, including the mechanisms for interleaving query routing with query parallelization and query execution process to continue the pruning at run-time. Comparing with the keyword-based indexing techniques adopted in most of the search engines and software, our approach offers fine-granularity of interest matching, thus it is more powerful and effective for handling queries with complex conditions.