Term proximity scoring is an established means in information retrieval for improving result quality of full-text queries. Integrating such proximity scores into efficient query processing, however, has not been equally well studied. Existing methods make use of precomputed lists of documents where tuples of terms, usually pairs, occur together, usually incurring a huge index size compared to term-only indexes. This article introduces a joint framework for trading off index size and result quality, and provides optimization techniques for tuning precomputed indexes towards either maximal result quality or maximal query processing performance under controlled result quality, given an upper bound for the index size. The framework allows to selectively materialize lists for pairs based on a query log to further reduce index size. Extensive experiments with two large text collections demonstrate runtime improvements of more than one order of magnitude over existing text-based processing techniques with reasonable index sizes.

Query processing with precomputed term pair lists can improve efficiency for some queries, but suffers from the quadratic number of index lists that need to be read. We present a novel hybrid index structure that aims at decreasing the number of index lists retrieved at query processing time, trading off a reduced number of index lists for an increased number of bytes to read. Our experiments demonstrate significant cold-cache performance gains of almost 25% on standard benchmark queries.

Proximity-aware scoring functions lead to significant effectiveness improvements for text retrieval. For XML IR, we can sometimes enhance the retrieval quality by exploiting knowledge about the document structure combined with established text IR methods. This paper introduces modified proximity scores that take the document structure into account and demonstrates the effect for the INEX benchmark.

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