A methodology for collection selection in heterogeneous contexts

In this paper we demonstrate that in an ideal Distributed Information Retrieval environment, taking the ability of each collection server to return relevant documents into account when selecting collections can be effective. Based on this assumption, we suggest a new approach to resolve the collection selection problem. In order to predict a collection's ability to return relevant documents, we inspect a limited number n of documents retrieved from each collection and analyze the proximity of search keywords within them. In our experiments, we vary the underlying parameter n of our suggested model to define the most appropriate number of top documents to be inspected. Moreover, we evaluate the retrieval effectiveness of our approach and compare it with both the centralized indexing and the CORI approaches [1], [16]. Preliminary results from these experiments, conducted on WT10g test collection, tend to demonstrate that our suggested method can achieve appreciable retrieval effectiveness.