A new techniques for optimizing the web search engines

A Web search engine must update its index periodically to incorporate changes to the Web. Query processing is a major cost factor in operating large web search engines. To solve this problem, we propose a framework for optimizing the search result by combing inverted index compression and feature-based caching. We perform a comparison and evaluation of several inverted compression algorithms. We then set a new feature-based eviction policies that achieve significant improvements over previous methods. Experimental results shows that this approach can achieve hit rate improvements.