Query routing in large-scale digital library systems

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. It 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 multilevel 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.