A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Static index pruning for information retrieval systems
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Web search efficiency via a locality based static pruning method
WWW '05 Proceedings of the 14th international conference on World Wide Web
Inverted files for text search engines
ACM Computing Surveys (CSUR)
A document-centric approach to static index pruning in text retrieval systems
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Pruning policies for two-tiered inverted index with correctness guarantee
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Boosting static pruning of inverted files
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Locality-Based pruning methods for web search
ACM Transactions on Information Systems (TOIS)
A Practitioner's Guide for Static Index Pruning
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Upper-bound approximations for dynamic pruning
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Information preservation in static index pruning
Proceedings of the 21st ACM international conference on Information and knowledge management
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As a query processing optimization technique over inverted index, static index pruning can significantly reduce index size and query processing time. A fast static index pruning algorithm is presented, which is a term-centric method and adopts BM25 weighting as the pruning measure. The algorithm scans through documents set with one pass and directly builds pruned index, and therefore avoids the construction of original index. The correctness of the algorithm is proved and the theoretical analysis reveals that its IO performance takes precedence over other algorithms. The experiments based on TREC data set also show that the fast static index pruning algorithm requires less time to build pruned index, and the pruning effectiveness outperforms the baseline method.