Cluster-based language models for distributed retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The impact of database selection on distributed searching
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Generalizing GlOSS to Vector-Space Databases and Broker Hierarchies
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A Methodology for Collection Selection in Heterogeneous Contexts
ITCC '02 Proceedings of the International Conference on Information Technology: Coding and Computing
SUSHI: scoring scaled samples for server selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Server selection methods in personal metasearch: a comparative empirical study
Information Retrieval
Central-rank-based collection selection in uncooperative distributed information retrieval
ECIR'07 Proceedings of the 29th European conference on IR research
Reducing the uncertainty in resource selection
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Previous evaluations of server selection methods for federated search have either used metrics which are unconnected with user satisfaction, or have not been able to account for confounding factors due to other search components. We propose a new framework for evaluating federated search server selection techniques. In our model, we isolate the effect of other confounding factors such as server summaries and result merging. Our results suggest that state-of-the-art server selection techniques are generally effective but result merging methods can be significantly improved. Furthermore, we show that the performance differences among server selection techniques can be obscured by ineffective merging.