Document filtering for fast ranking
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Query evaluation: strategies and optimizations
Information Processing and Management: an International Journal
Self-indexing inverted files for fast text retrieval
ACM Transactions on Information Systems (TOIS)
Using sampled data and regression to merge search engine results
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Efficient query evaluation using a two-level retrieval process
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Pruned query evaluation using pre-computed impacts
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A pipelined architecture for distributed text query evaluation
Information Retrieval
Design trade-offs for search engine caching
ACM Transactions on the Web (TWEB)
Challenges in building large-scale information retrieval systems: invited talk
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Proceedings of the 2009 workshop on Web Search Click Data
Second ACM International Conference on Web Search and Web Data Mining
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
Improved techniques for result caching in web search engines
Proceedings of the 18th international conference on World wide web
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Cost-Aware Strategies for Query Result Caching in Web Search Engines
ACM Transactions on the Web (TWEB)
Upper-bound approximations for dynamic pruning
ACM Transactions on Information Systems (TOIS)
Query efficiency prediction for dynamic pruning
Proceedings of the 9th workshop on Large-scale and distributed informational retrieval
Universal codeword sets and representations of the integers
IEEE Transactions on Information Theory
Scheduling queries across replicas
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Efficient and effective retrieval using selective pruning
Proceedings of the sixth ACM international conference on Web search and data mining
Hybrid query scheduling for a replicated search engine
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Load-sensitive selective pruning for distributed search
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A self-adapting latency/power tradeoff model for replicated search engines
Proceedings of the 7th ACM international conference on Web search and data mining
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Dynamic pruning strategies permit efficient retrieval by not fully scoring all postings of the documents matching a query -- without degrading the retrieval effectiveness of the top-ranked results. However, the amount of pruning achievable for a query can vary, resulting in queries taking different amounts of time to execute. Knowing in advance the execution time of queries would permit the exploitation of online algorithms to schedule queries across replicated servers in order to minimise the average query waiting and completion times. In this work, we investigate the impact of dynamic pruning strategies on query response times, and propose a framework for predicting the efficiency of a query. Within this framework, we analyse the accuracy of several query efficiency predictors across 10,000 queries submitted to in-memory inverted indices of a 50-million-document Web crawl. Our results show that combining multiple efficiency predictors with regression can accurately predict the response time of a query before it is executed. Moreover, using the efficiency predictors to facilitate online scheduling algorithms can result in a 22% reduction in the mean waiting time experienced by queries before execution, and a 7% reduction in the mean completion time experienced by users.