Methods for information server selection

doi:10.1145/297117.297123

Methods for information server selection

1999-01-01

45

OA

AI

DOI：10.1145/297117.297123

原文链接

查看原文

摘要

En 中文

The problem of using a broker to select a subset of available information servers in order to achieve a good trade-off between document retrieval effectiveness and cast is addressed. Sewer selection methods which are capable of operating in the absence of global information, and where sewers have no knowledge of brokers, are investigated. A novel method using Lightweight Probe queries (LWP method) is compared with several methods based on data from past query processing, while Random and Optimal server rankings serve as controls. Methods are evaluated, using TREC data and relevance judgments, by computing ratios, both empirical and ideal, of recall and early precision for the subset versus the complete set of available servers. Estimates are also made of the best-possible performance of each of the methods. LWP and Topic Similarity methods achieved best results, each being capable of retrieving about 60% of the relevant documents for only one-third of the cost of querying all servers. Subject to the applicable cost model, the LWP method is likely to be preferred because it is suited to dynamic environments. The good results obtained with a simple automatic LWP implementation were replicated using different data and a larger set of query topics.

Keyword:

design

experimentation

performance

information servers

Lightweight Probe queries

network servers

server ranking

server selection

text retrieval

Methods for information server selection

摘要

AI总结

期刊

机构

引用论文