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Abstract: inverted index traversal techniques have been studied in addressing the query processing performance challenges of web search engines, but still leave much room for improvement. In this paper, we focus on the inverted index traversal on document-sorted indexes and the optimization technique called dynamic pruning, which can efficiently reduce the hardware computational resources required. We propose another novel exhaustive index traversal scheme called largest scores first (LSF) retrieval, in which the candidates are first selected in the posting list of important query terms with the largest upper bound scores and then fully scored with the contribution of the remaining query terms. The scheme can effectively reduce the memory consumption of existing term-at-a-time (TAAT) and the candidate selection cost of existing document-at-a-time (DAAT) retrieval at the expense of revisiting the posting lists of the remaining query terms. Preliminary analysis and implementation show comparable performance between LSF and the two well-known baselines. To further reduce the number of postings that need to be revisited, we present efficient rank safe dynamic pruning techniques based on LSF, including two important optimizations called list omitting (LSF_LO) and partial scoring (LSF_PS) that make full use of query term importance. Finally, experimental results with the TREC GOV2 collection show that our new index traversal approaches reduce the query latency by almost 27% over the WAND baseline and produce slightly better results compared with the MaxScore baseline, while returning the same results as exhaustive evaluation.

This paper studies the traversal on inverted index to support efficient top-k search queries. The focus is on document-sorted inverted indexes, and the authors propose a new index traversal algorithm (LSF) and two associated dynamic pruning techniques to reduce the search space and memory consumption in practice. Moreover, the pruning technique is rank safe, so that the results would be the same as if an exhaustive search is performed. Experiments are performend with TREC GOV2 collection, where the two proposed pruning techniques are compared with two popular ones in the literature (WAND and MaxScore), and the results show that one of the proposed pruning techniques (LSF_PS) improves WAND by 27% in query latency, and has slightly better performance than MaxScore. The paper is very well-written, and the results are intereseting.

基于最大重要度优先查询的动态剪枝算法

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