DMCA
Learning to Distribute Queries into Web Search Nodes
BibTeX
@MISC{Mendoza_learningto,
author = {Marcelo Mendoza and Mauricio Marín and Flavio Ferrarotti and Barbara Poblete},
title = {Learning to Distribute Queries into Web Search Nodes},
year = {}
}
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Abstract
Abstract. Web search engines are composed of a large set of search nodes and a broker machine that feeds them with queries. A location cache keeps minimal information in the broker to register the search nodes capable of producing the top-N results for frequent queries. In this paper we show that it is possible to use the location cache as a training dataset for a standard machine learning algorithm and build a predictive model of the search nodes expected to produce the best approximated results for queries. This can be used to prevent the broker from sending queries to all search nodes under situations of sudden peaks in query traffic and, as a result, avoid search node saturation. This paper proposes a logistic regression model to quickly predict the most pertinent search nodes for a given query. 1
Keyphrases
web search node search node location cache approximated result minimal information avoid search node saturation broker machine frequent query top-n result standard machine web search engine query traffic pertinent search node training dataset logistic regression model large set predictive model sudden peak
