Adaptive query-based sampling for distributed IR

Abstract

In Distributed Information Retrieval systems (DIR), the widely accepted solution for resource description acquisition is Query-Based Sampling (QBS) [1]. In the standard approach to QBS, once 300-500 unique documents have been retrieved sampling is curtailed. This threshold was obtained by empirically measuring the estimated resource description against the actual resource, and then considering the corresponding retrieval selection accuracy [1]. However, a fixed threshold may not generalise to other collections and environments beyond that which it was estimated on (i.e. a set of resources of uniform size [1]). Cases when the blanket application of such a heuristic would be inappropriate include (1) when the sizes of resource are highly skewed and (2) when the resources are very heterogenous. In the former, if a resource is very large then undersampling will occur because not enough documents were obtained. Conversely, if a collection is very small in size, then oversampling will occur increasing costs beyond necessity. In the later case, if the resource is varied and highly heterogeneous, then to obtain a sufficiently accurate description would require more documents to be sampled than when resources are homogenous. Either way, adopting a flat cut off will not necessarily provide sufficiently good resource descriptions for all resources.