Adaptive query-based sampling of distributed collections

Baillie, M., Azzopardi, L. and Crestani, F. (2006) Adaptive query-based sampling of distributed collections. Lecture Notes in Computer Science, 4209, pp. 316-328. (doi:10.1007/11880561_26)

Full text not currently available from Enlighten.

Abstract

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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Azzopardi, Dr Leif
Authors: Baillie, M., Azzopardi, L., and Crestani, F.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
ISSN:0302-9743
ISSN (Online):1611-3349

University Staff: Request a correction | Enlighten Editors: Update this record