Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
A decision-theoretic approach to database selection in networked IR
ACM Transactions on Information Systems (TOIS)
GlOSS: text-source discovery over the Internet
ACM Transactions on Database Systems (TODS)
Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Server Ranking for Distributed Text Retrieval Systems on the Internet
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
ACM SIGIR Forum
Combining document representations for known-item search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Evaluating different methods of estimating retrieval quality for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Comparing the performance of collection selection algorithms
ACM Transactions on Information Systems (TOIS)
A semisupervised learning method to merge search engine results
ACM Transactions on Information Systems (TOIS)
Engineering a multi-purpose test collection for web retrieval experiments
Information Processing and Management: an International Journal
Unified utility maximization framework for resource selection
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Server selection methods in hybrid portal search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Modeling search engine effectiveness for federated search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Capturing collection size for distributed non-cooperative retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Dynamic Programming: A Computational Tool (Studies in Computational Intelligence)
Dynamic Programming: A Computational Tool (Studies in Computational Intelligence)
Evaluating sampling methods for uncooperative collections
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Central-rank-based collection selection in uncooperative distributed information retrieval
ECIR'07 Proceedings of the 29th European conference on IR research
Distributed information retrieval and applications
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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In this paper, a new source selection algorithm for uncooperative distributed information retrieval environments is presented. The algorithm functions by modeling each information source as an integral, using the relevance score and the intra-collection position of its sampled documents in reference to a centralized sample index and selects the collections that cover the largest area in the rank-relevance space. Based on the above novel metric, the algorithm explicitly focuses on addressing the two goals of source selection; high recall which is important for source recommendation applications and high precision aiming to produce a high precision final merged list. For the latter goal in particular, the new approach steps away from the usual practice of DIR systems of explicitly declaring the number of collections that must be queried and instead receives as input only the number of retrieved documents in the final merged list, dynamically calculating the number of collections that are selected and the number of documents requested from each. The algorithm is tested in a wide range of testbeds in both recall and precision oriented settings and its effectiveness is found to be equal or better than other state-of-the-art algorithms.