Wenxiu Fang
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.
Supplemental Material
- Andrei Z Broder, David Carmel, Michael Herscovici, Aya Soffer, and Jason Zien. 2003. Efficient query evaluation using a two-level retrieval process. In Proc. CIKM. ACM, New Orleans, Louisiana, USA, 426--434.Google Scholar
Digital Library
- Matt Crane, J Shane Culpepper, Jimmy Lin, Joel Mackenzie, and Andrew Trotman. 2017. A comparison of Document-at-a-Time and Score-at-a-Time query evaluation. In Proc. WSDM. ACM, Cambridge, UK, 201--210.Google ScholarDigital Library
- Shuai Ding and Torsten Suel. 2011. Faster top-k document retrieval using blockmax indexes. In Proc. SIGIR. ACM, Beijing, China, 993--1002.Google Scholar
- Myeongjae Jeon, Saehoon Kim, Seung-won Hwang, Yuxiong He, Sameh Elnikety, Alan L Cox, and Scott Rixner. 2014. Predictive parallelization: Taming tail latencies in web search. In Proc. SIGIR. ACM, Queensland, Australia, 253--262.Google ScholarDigital Library
- Jimmy Lin and Andrew Trotman. 2015. Anytime ranking for impact-ordered indexes. In Proc. ICTIR. ACM, Northampton, MA, USA, 301--304.Google ScholarDigital Library
- Joel Mackenzie, J Shane Culpepper, Roi Blanco, Matt Crane, Charles LA Clarke, and Jimmy Lin. 2018. Query driven algorithm selection in early stage retrieval. In Proc. WSDM. ACM, Los Angeles, California, USA, 396--404.Google ScholarDigital Library
- Antonio Mallia, Giuseppe Ottaviano, Elia Porciani, Nicola Tonellotto, and Rossano Venturini. 2017. Faster BlockMax WAND with variable-sized blocks. In Proc. SIGIR. ACM, Shinjuku, Tokyo, Japan, 625--634.Google ScholarDigital Library
- Stephen E Robertson and K Sparck Jones. 1976. Relevance weighting of search terms. J. Am. Soc. Inf. Sci. 27, 3 (1976), 129--146.Google ScholarCross Ref
- Nicola Tonellotto, Craig Macdonald, and Iadh Ounis. 2013. Efficient and effective retrieval using selective pruning. In Proc. WSDM. ACM, Rome, Italy, 63--72.Google ScholarDigital Library
- Howard Turtle and James Flood. 1995. Query evaluation: strategies and optimizations. Inf. Process. Manag. 31, 6 (1995), 831--850.Google ScholarDigital Library
Index Terms
- Hybrid Dynamic Pruning for Efficient and Effective Query Processing
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