research-article

Top-k Query Processing with Conditional Skips

Authors:

Edward Bortnikov,

Guy Golan-GuetaAuthors Info & Claims

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

Pages 653 - 661

https://doi.org/10.1145/3041021.3054191

Published: 03 April 2017 Publication History

Abstract

This work improves the efficiency of dynamic pruning algorithms by introducing a new posting iterator that can skip large parts of the matching documents during top-k query processing. Namely, the conditional-skip iterator jumps to a target document while skipping all matching documents preceding the target that cannot belong to the final result list. We experiment with two implementations of the new iterator, and show that integrating it into representative dynamic pruning algorithms such as MaxScore, WAND, and Block Max WAND (BMW), reduces the document scoring overhead, and eventually the query latency.

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Cited By

Li ZMackenzie JChen HDuh WHuang HKato MMothe JPoblete B(2023)Profiling and Visualizing Dynamic Pruning AlgorithmsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591806(3125-3129)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591806
Shao JQiao YJi SYang TDiaz FShah CSuel TCastells PJones RSakai T(2021)Window Navigation with Adaptive Probing for Executing BlockMax WANDProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463109(2323-2327)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463109
Jiang KZhu LSun Q(2021)Optimizing Scoring and Sorting Operations for Faster WAND ProcessingAdvanced Data Mining and Applications10.1007/978-3-030-65390-3_38(499-514)Online publication date: 6-Jan-2021
https://doi.org/10.1007/978-3-030-65390-3_38
Show More Cited By

Index Terms

Top-k Query Processing with Conditional Skips
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Other conferences

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

April 2017

1738 pages

ISBN:9781450349147

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Sponsors

IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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WWW '17

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IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Companion Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Li ZMackenzie JChen HDuh WHuang HKato MMothe JPoblete B(2023)Profiling and Visualizing Dynamic Pruning AlgorithmsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591806(3125-3129)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591806
Shao JQiao YJi SYang TDiaz FShah CSuel TCastells PJones RSakai T(2021)Window Navigation with Adaptive Probing for Executing BlockMax WANDProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463109(2323-2327)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463109
Jiang KZhu LSun Q(2021)Optimizing Scoring and Sorting Operations for Faster WAND ProcessingAdvanced Data Mining and Applications10.1007/978-3-030-65390-3_38(499-514)Online publication date: 6-Jan-2021
https://doi.org/10.1007/978-3-030-65390-3_38
Khattab OHammoud MElsayed THuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Finding the Best of Both WorldsProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401076(1031-1040)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401076
Sheffi GBasin DBortnikov ECarmel DKeidar IGupta RShen X(2020)Scalable top-k retrieval with SpartaProceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming10.1145/3332466.3374522(62-73)Online publication date: 19-Feb-2020
https://dl.acm.org/doi/10.1145/3332466.3374522
Shan XWang GDing LSong BXu Y(2018)Top-$k$ Subgraph Query Based on Frequent Structure in Large-Scale Dynamic GraphsIEEE Access10.1109/ACCESS.2018.28850386(78471-78482)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2885038

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