As part of a Distributed Information Retrieval system a de-scription of each remote information resource, archive or repository is usually stored centrally in order to facilitate resource selection. The ac-quisition ofprecise resourcedescriptionsistherefore animportantphase in Distributed Information Retrieval, as the quality of such represen-tations will impact on selection accuracy, and ultimately retrieval per-formance. 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 thequality of an acquired resource description estimate, and when a suﬃciently good representation of a resource hasbeen obtained during Query-Based Sampling.
|Number of pages||12|
|Publication status||Published - 2006|
|Event||13th Symposium on String Processing and Information Retrieval (SPIRE 2006) - Glasgow, UK|
Duration: 11 Oct 2006 → 13 Oct 2006
|Conference||13th Symposium on String Processing and Information Retrieval (SPIRE 2006)|
|Period||11/10/06 → 13/10/06|
- distributed information retrieval
- query-based sampling
Baillie, M., Azzopardi, L., & Crestani, F. (2006). Adaptive query-based sampling of distributed collections. 316-328. Paper presented at 13th Symposium on String Processing and Information Retrieval (SPIRE 2006), Glasgow, UK, .