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CLC number: TP393

On-line Access: 2016-01-05

Received: 2015-06-06

Revision Accepted: 2015-10-14

Crosschecked: 2015-12-24

Cited: 1

Clicked: 7385

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Kun Jiang

http://orcid.org/0000-0003-1316-5237

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.1 P.1-14

http://doi.org/10.1631/FITEE.1500190


Efficient dynamic pruning on largest scores first (LSF) retrieval


Author(s):  Kun Jiang, Yue-xiang Yang

Affiliation(s):  College of Computer, National University of Defense Technology, Changsha 410073, China

Corresponding email(s):   jk_365@126.com

Key Words:  Inverted index, Index traversal, Query latency, Largest scores first (LSF) retrieval, Dynamic pruning


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Kun Jiang, Yue-xiang Yang. Efficient dynamic pruning on largest scores first (LSF) retrieval[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(1): 1-14.

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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

目的:不断增长的网页数量和查询请求量对搜索引擎的查询性能提出非常大的挑战。当前索引遍历算法存在的大量候选文档失效问题依然制约着搜索引擎查询性能的提升。本文通过研究倒排索引的遍历方式和动态剪枝算法来加快搜索引擎top-k查询处理的性能。
创新点:提出最大重要度优先(Largest Scores First,LSF)查询算法,使得具有较高重要度的查询词项所指向的倒排链表能够优先得到处理。提出两种精确的动态剪枝算法:基于LSF的去除倒排链表技术(List Omitting,LSF_LO)和基于LSF的文档部分打分技术(Partial Scoring,LSF_PS)。
方法:首先,通过对现有动态剪枝算法的对比分析得出词项重要度对于搜索引擎top-k查询性能的影响:优先处理重要度较高的查询词项能够快速提升结果集的阈值,从而避免对估计得分较低的文档的处理。其次,通过设计倒排链表实体的各种操作方法来实现对倒排链表按照最大重要度的排序和处理,给出算法的伪码并分析了算法的计算复杂度。最后,利用最大重要度优先查询算法在top-k查询中的优势,实时估计每个倒排项在每计算一个词项的贡献之后的最大可能分数,同时在一个倒排链表遍历结束后估计其剩余最大可能贡献分数,避免对于估计最大得分低于结果集阈值的文档的各种处理操作,从而达到对搜索引擎top-k查询性能的提升。
结论:提出了LSF查询和其上的两种动态剪枝算法LSF_LO和LSF_PS。实验结果表明本文所提LSF查询相比传统DAAT查询在性能上有了明显的提升。

关键词:倒排索引;索引遍历;查询延迟;最大重要度优先查询;动态剪枝

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