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基于时空局部性的层次化查询结果缓存机制
引用本文:朱亚东,郭嘉丰,兰艳艳,程学旗.基于时空局部性的层次化查询结果缓存机制[J].中文信息学报,2016,30(1):63-71.
作者姓名:朱亚东  郭嘉丰  兰艳艳  程学旗
作者单位:1. 中国科学院 计算技术研究所 中国科学院网络数据科学与技术重点实验室,北京 100190;
2. 中国科学院大学,北京 100049)
基金项目:国家973计划(2014CB340401,2012CB316303);国家863计划(2014AA015204);国家自然科学基金(61472401,61433014,61425016,61203298,61572473)
摘    要:查询结果缓存可以对查询结果的文档标识符集合或者实际的返回页面进行缓存,以提高用户查询的响应速度,相应的缓存形式可以分别称之为标识符缓存或页面缓存。对于固定大小的内存,标识符缓存可以获得更高的命中率,而页面缓存可以达到更高的响应速度。该文根据用户查询访问的时间局部性和空间局部性,提出了一种新颖的基于时空局部性的层次化结果缓存机制。首先,该机制将固定大小的结果缓存划分为两层:页面缓存和标识符缓存。对于用户提交的查询,该机制会首先使用第一层的页面缓存进行应答,如果未能命中,则继续尝试使用第二层的标识符缓存。实验显示这种层次化的缓存机制较传统的仅依赖于单一缓存形式的机制,在平均查询响应时间上,取得了可观的性能提升:例如,相对单纯的页面缓存,平均达到9%,最好情况下达到11%。其次,该机制在标识符缓存的基础上,设计了一种启发式的预取策略,对用户查询检索的空间局部性进行挖掘。实验显示,这种预取策略的融合,能进一步促进检索系统性能的有效提升,从而最终建立起一套时空完备的、有效的结果缓存机制。

关 键 词:页面缓存  标识符缓存  启发式预取  />  

A Hierarchical Search Result Caching Based on Temporal and Spatial Locality
ZHU Yadong,GUO Jiafeng,LAN Yanyan,CHENG Xueqi.A Hierarchical Search Result Caching Based on Temporal and Spatial Locality[J].Journal of Chinese Information Processing,2016,30(1):63-71.
Authors:ZHU Yadong  GUO Jiafeng  LAN Yanyan  CHENG Xueqi
Affiliation:1. CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology,
Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:In a result cache, either document identifiers (docID cache) or the actual HTML pages (page cache) can be stored to accelerate the response speed. For a fixed memory size, the docID cache can achieve a higher hit ratio while the page cache can obtain higher response speed. This paper proposes a novel hierarchical result caching scheme based on temporal and spatial locality, in which the result cache is firstly split into two layersa page cache and a docID cache. In our scheme, page cache will be the first choice for answering some queries, and then the docID cache. In terms of average query response time, the results show that the proposed approach achieves a substantial performance improvement than baseline method by 9% on average, and up to 11% in the best situation. Secondly, the scheme also designs an adaptive prefetching strategy based on docID cache. The experiments show that the proposed scheme combined with the prefetching strategy can lead to an additional performance improvement. And we finally build a complete and effective result caching scheme by capturing the temporal and spatial locality of user search behaviours.
Keywords:page cache  DocID cache  query response time  
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