Integration of probabilistic fact and text retrieval
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Overview of the first TREC conference
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
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 probabilistic model for distributed information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating database selection techniques: a testbed and experiment
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic solution to the selection and fusion problem in distributed information retrieval
Proceedings of the 22nd 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)
Multimedia Information Retrieval: Content-Based Information Retrieval from Large Text and Audio Databases
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Determining Text Databases to Search in the Internet
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Generalizing GlOSS to Vector-Space Databases and Broker Hierarchies
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Studying the clustering paradox and scalability of search in highly distributed environments
ACM Transactions on Information Systems (TOIS)
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This paper describes a probabilistic model for optimum information retrieval in a distributed heterogeneous environment.The model assumes the collection of documents offered by the environment to be partitioned into subcollections. Documents as well as subcollections have to be indexed, where indexing methods using different indexing vocabularies can be employed. A query provided by a user is answered in terms of a ranked list of documents. The model determines a procedure for ranking the documents that stems from the Probability Ranking Principle: For each subcollection, the subcollection's documents are ranked; the resulting ranked lists are combined into a final ranked list of documents, where the ordering is determined by the documents' probabilities of being relevant with respect to the user's query. Various probabilistic ranking methods may be involved in the distributed ranking process. A criterion for effectively limiting the ranking process to a subset of subcollections extends the model.The property that different ranking methods and indexing vocabularies can be used is important when the subcollections are heterogeneous with respect to their content.The model's applicability is experimentally confirmed. When exploiting the degrees of freedom provided by the model, experiments showed evidence that the model even outperforms comparable models for the non-distributed case with respect to retrieval effectiveness.