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Influence sets based on reverse nearest neighbor queries

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S. MuthukrishnanAuthors Info & Claims

SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data

Pages 201 - 212

https://doi.org/10.1145/342009.335415

Published: 16 May 2000 Publication History

Abstract

Inherent in the operation of many decision support and continuous referral systems is the notion of the “influence” of a data point on the database. This notion arises in examples such as finding the set of customers affected by the opening of a new store outlet location, notifying the subset of subscribers to a digital library who will find a newly added document most relevant, etc. Standard approaches to determining the influence set of a data point involve range searching and nearest neighbor queries.

In this paper, we formalize a novel notion of influence based on reverse neighbor queries and its variants. Since the nearest neighbor relation is not symmetric, the set of points that are closest to a query point (i.e., the nearest neighbors) differs from the set of points that have the query point as their nearest neighbor (called the reverse nearest neighbors). Influence sets based on reverse nearest neighbor (RNN) queries seem to capture the intuitive notion of influence from our motivating examples.

We present a general approach for solving RNN queries and an efficient R-tree based method for large data sets, based on this approach. Although the RNN query appears to be natural, it has not been studied previously. RNN queries are of independent interest, and as such should be part of the suite of available queries for processing spatial and multimedia data. In our experiments with real geographical data, the proposed method appears to scale logarithmically, whereas straightforward sequential scan scales linearly. Our experimental study also shows that approaches based on range searching or nearest neighbors are ineffective at finding influence sets of our interest.

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Cited By

Chen JGao HZhang KWang JLuo YWu ZLi J(2024)On Efficiently Processing MIT Queries in Trajectory DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336194836:7(3329-3347)Online publication date: Jul-2024
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Huang QWang YTung AWilliams BChen YNeville J(2023)SAHProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25550(4312-4321)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i4.25550
Huang YLin L(2023)Evaluating Neighboring Site Group Queries with Constraint of Budget RangeProceedings of the 2023 11th International Conference on Computer and Communications Management10.1145/3617733.3617735(6-11)Online publication date: 4-Aug-2023
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Index Terms

Influence sets based on reverse nearest neighbor queries
1. Information systems
  1. Data management systems
    1. Query languages
  2. Information systems applications

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cover image ACM Conferences

SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data

May 2000

604 pages

ISBN:1581132174

DOI:10.1145/342009

Chairmen:
Maggie Dunham
Southern Methodist Univ.
,
Jeffrey F. Naughton
Univ. of Wisconsin-Madison
,
Weidong Chen
Southern Methodist Univ.
,
Nick Koudas
AT &T Labs

ACM SIGMOD Record Volume 29, Issue 2
June 2000
609 pages
ISSN:0163-5808
DOI:10.1145/335191
Editors:
Weidong Chen
Southern Methodist Univ., Dallas, TX
,
Jeffrey Naughton
Univ. of Wisconsin-Madison, Madison
,
Philip A. Bernstein
Microsoft
Issue’s Table of Contents

Copyright © 2000 ACM.

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Published: 16 May 2000

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SIGMOD/PODS00: ACM International Conference on Management of Data and Symposium on Principles of Database Systems

May 15 - 18, 2000

Texas, Dallas, USA

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SIGMOD '00 Paper Acceptance Rate 42 of 248 submissions, 17%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Chen JGao HZhang KWang JLuo YWu ZLi J(2024)On Efficiently Processing MIT Queries in Trajectory DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336194836:7(3329-3347)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3361948
Huang QWang YTung AWilliams BChen YNeville J(2023)SAHProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25550(4312-4321)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i4.25550
Huang YLin L(2023)Evaluating Neighboring Site Group Queries with Constraint of Budget RangeProceedings of the 2023 11th International Conference on Computer and Communications Management10.1145/3617733.3617735(6-11)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1145/3617733.3617735
Han LLuo WYang YYang ALu RLai JZheng Y(2023)PPOLQ: Privacy-Preserving Optimal Location Query With Multiple-Condition Filter in Outsourced EnvironmentsIEEE Transactions on Services Computing10.1109/TSC.2023.327320816:5(3564-3577)Online publication date: Sep-2023
https://doi.org/10.1109/TSC.2023.3273208
Zheng YZhu HLu RGuan YZhang SWang FShao JLi H(2023)Efficient and Privacy-Preserving Aggregated Reverse kNN Query Over Crowd-Sensed DataIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.329341618(4285-4299)Online publication date: 2023
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Chen JGao HZhang KWang JLuo YWu ZLi J(2023)Towards Efficient MIT query in Trajectory Data2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00170(2194-2206)Online publication date: Apr-2023
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Cho H(2022)Cluster Nested Loop k-Farthest Neighbor Join Algorithm for Spatial NetworksISPRS International Journal of Geo-Information10.3390/ijgi1102012311:2(123)Online publication date: 9-Feb-2022
https://doi.org/10.3390/ijgi11020123
Li XXiang TGuo SLi HMu Y(2022)Privacy-Preserving Reverse Nearest Neighbor Query Over Encrypted Spatial DataIEEE Transactions on Services Computing10.1109/TSC.2021.306535615:5(2954-2968)Online publication date: 1-Sep-2022
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Qiu TLi Y(2022)Fast LDP-MST: an efficient density-peak-based clustering method for large-size datasetsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3150403(1-1)Online publication date: 2022
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Tang BMouratidis KHan MLi GLi ZIdreos SSrivastava D(2021)On m-Impact Regions and Standing Top-k Influence ProblemsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452832(1784-1796)Online publication date: 9-Jun-2021
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