Efficient query evaluation using a two-level retrieval process
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Optimization strategies for complex queries
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
IO-Top-k: index-access optimized top-k query processing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Optimized query execution in large search engines with global page ordering
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Inverted index compression and query processing with optimized document ordering
Proceedings of the 18th international conference on World wide web
Probabilistic static pruning of inverted files
ACM Transactions on Information Systems (TOIS)
Interval-based pruning for top-k processing over compressed lists
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Posting list intersection on multicore architectures
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Faster top-k document retrieval using block-max indexes
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Intra-query concurrent pipelined processing for distributed full-text retrieval
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
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Large Web search engines are complex systems that solve thousands of user queries per second on clusters of dedicated distributed memory processors. Processing each query involves executing a number of operations to get the answer presented to the user. The most expensive operation in running time is the calculation of the top-k documents that best match each query. In this paper we propose the parallelization of a state of the art document ranking algorithm called Block-Max WAND. We propose a 2-steps parallelization of the WAND algorithm in order to reduce inter-processor communication and running time cost. Multi-threading tailored to Block-Max WAND is also proposed to exploit multi-core parallelism in each processor. The experimental results show that the proposed parallelization reduces execution time significantly as compared against current approaches used in search engines.