缓冲是一种重要技术提高质问处理的效率。不幸地,因为存储空间,传统的缓冲机制不为深网是有效的;动态维护限制。在这篇论文,我们在为深网质问基于 Top-K 数据来源(KDS 厘米) 提供缓存机制而不是结果记录上在场。由从红外集成技术;Top-K,数据重组策略被介绍为 KDS 厘米建模。另外关于缓存管理的一些措施;优化被建议有效地改进缓存的表演。试验性的结果在实行费用显示出 KDS 厘米的好处;动态维护什么时候与各种各样的交替的策略相比。
Caching is an important technique to enhance the efficiency of query processing. Unfortunately, traditional caching mechanisms are not efficient for deep Web because of storage space and dynamic maintenance limitations. In this paper, we present on providing a cache mechanism based on Top-K data source (KDS-CM) instead of result records for deep Web query. By integrating techniques from IR and Top-K, a data reorganization strategy is presented to model KDS-CM. Also some measures about cache management and optimization are proposed to improve the performances of cache effectively. Experimental results show the benefits of KDS-CM in execution cost and dynamic maintenance when compared with various alternate strategies.