RAM and dynamic pruning schemes to reduce query evaluation times. While only a small portion of lists are processed with dynamic pruning, current systems still store the entire inverted list in cache. In this paper we investigate caching only the pieces of the inverted lists that are actually used to answer a query during dynamic pruning. We examine an LRU cache model, and two recently proposed models. We also introduce a new dynamic pruning scheme for impact-ordered inverted lists.

The presentation of query biased document snippets as part of results pages presented by search engines has become an expectation of search engine users. In this paper we explore the algorithms and data structures required as part of a search engine to allow efficient generation of query biased snippets. We begin by proposing and analysing a document compression method that reduces snippet generation time by 58% over a baseline using the zlib compression library. These experiments reveal that finding documents on secondary storage dominates the total cost of generating snippets, and so caching documents in RAM is essential for a fast snippet generation process. Using simulation, we examine snippet generation performance for different size RAM caches. Finally we propose and analyse document reordering and compaction, revealing a scheme that increases the number of document cache hits with only a marginal affect on snippet quality. This scheme effectively doubles the number of documents that can fit in a fixed size cache.