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Abstract

n the presence of growing data, the need for efficient query processing under
result quality and index size control becomes more and more a challenge to
search engines. We show how to use proximity scores to make query processing
effective and efficient with focus on either of the optimization goals.
More precisely, we make the following contributions:
• We present a comprehensive comparative analysis of proximity score models and
a rigorous analysis of the potential of phrases and adapt a leading proximity
score model for XML data.
• We discuss the feasibility of all presented proximity score models for top-k
query processing and present a novel index combining a content and proximity
score that helps to accelerate top-k query processing and improves result
quality.
• We present a novel, distributed index tuning framework for term and term pair
index lists that optimizes pruning parameters by means of well-defined
optimization criteria under disk space constraints. Indexes can be tuned with
emphasis on efficiency or effectiveness: the resulting indexes yield fast
processing at high result quality.
• We show that pruned index lists processed with a merge join outperform top-k
query processing with unpruned lists at a high result quality.
• Moreover, we present a hybrid index structure for improved cold cache run
times.