document: Improving Web search efficiency via a locality based static pruning method The unarguably fast , and continuous , growth of the volume of indexed ( and indexable ) documents on the Web poses a great challenge for search engines . This is true regarding not only search effectiveness but also time and space efficiency . In this paper we present an index pruning technique targeted for search engines that addresses the latter issue without disconsidering the former . To this effect , we adopt a new pruning strategy capable of greatly reducing the size of search engine indices . Experiments using a real search engine show that our technique can reduce the indices ' storage costs by up to 60 % over traditional lossless compression methods , while keeping the loss in retrieval precision to a minimum . When compared to the indices size with no compression at all , the compression rate is higher than 88 % , i.e. , less than one eighth of the original size . More importantly , our results indicate that , due to the reduction in storage overhead , query processing time can be reduced to nearly 65 % of the original time , with no loss in average precision . The new method yields significative improvements when compared against the best known static pruning method for search engine indices . In addition , since our technique is orthogonal to the underlying search algorithms , it can be adopted by virtually any search engine .
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