Efficient Query Processing in Distributed Search Engines

Web search engines have to deal with a rapidly increasing amount of information, high query loads and tight performance constraints. The success of a search engine depends on the speed with which it answers queries (efficiency) and the quality of its answers (effectiveness). These two metrics have a large impact on the operational costs of the search engine and the overall user satisfaction, which determine the revenue of the search engine. In this context, any improvement in query processing efficiency can reduce the operational costs and improve user satisfaction, hence improve the overall benefit. In this thesis, we elaborate on query processing efficiency, address several problems within partitioned query processing, pruning and caching and propose several novel techniques: First, we look at term-wise partitioned indexes and address the main limitations of the state-of-the-art query processing methods. Our first approach combines the advantage of pipelined and traditional (non-pipelined) query processing. This approach assumes one disk access per posting list and traditional term-at-a-time processing. For the sec-ond approach, we follow an alternative direction and look at document-at-a-time process