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Full Description
Every day, millions of users rely on search engines to satisfy the information needs required for performing many routine tasks. The effectiveness and efficiency of a search engine are two prime goals that form a natural trade-off. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency.
This survey comprehensively reviews the foundations of search engines, from index layouts to basic query processing strategies, while also providing the latest trends in the literature in efficient query processing. It goes on to describe techniques in applying a cascading infrastructure within search systems, such as learned models obtained from learning-to-rank techniques. The survey also covers the selective application of query processing techniques to ensure that the required retrieval speed targets can be met. Finally, the authors bring the reader completely up-to-date by describing techniques for the efficient deployment of learned models in a multi-stage ranking system. Efficient Query Processing for Scalable Web Search will be a valuable reference for researchers and developers working on cutting edge of web system design where effective and efficient search is an integral part of the design.
Contents
1. Introduction
2. Modern Infrastructure Foundations
3. Dynamic Pruning Query Processing
4. Query Efficiency Prediction for Dynamic Pruning
5. Impact-Sorted Indexes
6. Learning-to-Rank and Cascades
7. Open Directions and Conclusions
Acknowledgements
References
