Adaptive query-based sampling of distributed collections

As part of a Distributed Information Retrieval system a description
of each remote information resource, archive or repository is
usually stored centrally in order to facilitate resource selection. The acquisition
of precise resource descriptions is therefore an important phase
in Distributed Information Retrieval, as the quality of such representations
will impact on selection accuracy, and ultimately retrieval performance.
While Query-Based Sampling is currently used for content
discovery of uncooperative resources, the application of this technique is
dependent upon heuristic guidelines to determine when a sufficiently accurate
representation of each remote resource has been obtained. In this
paper we address this shortcoming by using the Predictive Likelihood to
provide both an indication of the quality of an acquired resource description
estimate, and when a sufficiently good representation of a resource
has been obtained during Query-Based Sampling.