Optimized Query Execution in Large Search Engines with Global Page Ordering.

Large web search engines have to answer thousands of queries per second with interactive response times. A major factor in the cost of executing a query is given by the lengths of the inverted lists for the query terms, which increase with the size of the document collection and are often in the range of many megabytes. To address this issue, IR and database researchers have proposed pruning techniques that compute or approximate term-based ranking functions without scanning over the full inverted lists. Over the last few years, search engines have incorporated new types of ranking techniques that exploit aspects such as the hyperlink structure of the web or the popularity of a page to obtain improved results. We focus on the question of how such techniques can be efficiently integrated into query processing. In particular, we study pruning techniques for query execution in large engines in the case where we have a global ranking of pages, as provided by Pagerank or any other method, in addition to the standard term-based approach. We describe pruning schemes for this case and evaluate their efficiency on an experimental cluster-based search engine with million web pages. Our results show that there is significant potential benefit in such techniques.