Hybrid Dynamic Pruning for Efficient and Effective Query Processing

Wenxiu Fang, Trent G. Marbach, Gang Wang, Xiaoguang Liu

Abstract: The performance of query processing has always been a concern in the field of information retrieval. Dynamic pruning algorithms have been proposed to improve query processing performance in terms of efficiency and effectiveness. However, a single pruning algorithm generally does not have both advantages. In this work, we investigate the performance of the main dynamic pruning algorithms in terms of average and tail latency as well as the accuracy of query results, and find that they are complementary. Inspired by these findings, we propose two types of hybrid dynamic pruning algorithms that choose different combinations of strategies according to the characteristics of each query. Experimental results demonstrate that our proposed methods yield a good balance between both efficiency and effectiveness.