Probabilistic information retrieval in a distributed heterogeneous environment

This thesis 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 hierarchically partitioned into subcollections. Documents as well as subcollections have to be indexed. At this 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 elements 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. The underlying data volume is arbitrarily scalable. A criterion for effectively limiting the ranking process to a subset of subcollections extends the model. 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. An architecture for a distributed information retrieval system is presented that realizes the probabilistic model. The system provides access to an arbitrary number of dynamic multimedia databases