Building a high-level dataflow system on top of Map-Reduce: the Pig experience
Proceedings of the VLDB Endowment
Fast query expansion using approximations of relevance models
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
On upper bounds for dynamic pruning
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Efficiency optimizations for interpolating subqueries
Proceedings of the 20th ACM international conference on Information and knowledge management
Optimizing top-k document retrieval strategies for block-max indexes
Proceedings of the sixth ACM international conference on Web search and data mining
An incremental approach to efficient pseudo-relevance feedback
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A candidate filtering mechanism for fast top-k query processing on modern cpus
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Text search systems research has primarily focused on simple occurrences of query terms within documents to compute document relevance scores. However, recent research shows that additional document features are crucial for improving retrieval effectiveness. We develop a series of techniques for efficiently processing queries with feature-based models. Our TupleFlow framework, an extension of MapReduce, provides a basis for custom binned indexes, which efficiently store feature data. Our work in binning probabilities shows how to effectively map language model probabilities into the space of small positive integers, which helps improve speeds without reducing query effectiveness. We also show new efficient query processing results for both document-sorted and score-sorted indexes. All of our work is evaluated using the largest available research dataset.