Server selection methods in personal metasearch: a comparative empirical study |
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| Authors: | Paul Thomas David Hawking |
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| Institution: | (1) CSIRO ICT Centre, GPO Box 664, Canberra, ACT, 2601, Australia;(2) Funnelback Pty Ltd, P.O. Box 1441, Dickson, ACT, 2601, Australia |
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| Abstract: | Server selection is an important subproblem in distributed information retrieval (DIR) but has commonly been studied with
collections of more or less uniform size and with more or less homogeneous content. In contrast, realistic DIR applications
may feature much more varied collections. In particular, personal metasearch—a novel application of DIR which includes all
of a user’s online resources—may involve collections which vary in size by several orders of magnitude, and which have highly
varied data. We describe a number of algorithms for server selection, and consider their effectiveness when collections vary
widely in size and are represented by imperfect samples. We compare the algorithms on a personal metasearch testbed comprising
calendar, email, mailing list and web collections, where collection sizes differ by three orders of magnitude. We then explore
the effect of collection size variations using four partitionings of the TREC ad hoc data used in many other DIR experiments.
Kullback-Leibler divergence, previously considered poorly effective, performs better than expected in this application; other
techniques thought to be effective perform poorly and are not appropriate for this problem. A strong correlation with size-based
rankings for many techniques may be responsible. |
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