research-article

Efficient search for the top-k probable nearest neighbors in uncertain databases

Authors:

George Beskales,

Mohamed A. Soliman,

Ihab F. IlyasAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 1, Issue 1

Pages 326 - 339

https://doi.org/10.14778/1453856.1453895

Published: 01 August 2008 Publication History

Abstract

Uncertainty pervades many domains in our lives. Current real-life applications, e.g., location tracking using GPS devices or cell phones, multimedia feature extraction, and sensor data management, deal with different kinds of uncertainty. Finding the nearest neighbor objects to a given query point is an important query type in these applications.

In this paper, we study the problem of finding objects with the highest marginal probability of being the nearest neighbors to a query object. We adopt a general uncertainty model allowing for data and query uncertainty. Under this model, we define new query semantics, and provide several efficient evaluation algorithms. We analyze the cost factors involved in query evaluation, and present novel techniques to address the trade-offs among these factors. We give multiple extensions to our techniques including handling dependencies among data objects, and answering threshold queries. We conduct an extensive experimental study to evaluate our techniques on both real and synthetic data.

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Cited By

Züfle A(2024)Probabilistic Counting in Uncertain Spatial Databases Using Generating FunctionsSpatial Gems, Volume 210.1145/3617291.3617300(85-96)Online publication date: 25-Jan-2024
(2024)Spatial Gems, Volume 2undefinedOnline publication date: 25-Jan-2024
Emrich TKriegel HZüfle AKröger PRenz M(2022)Complete and Sufficient Spatial Domination of Multidimensional RectanglesSpatial Gems, Volume 110.1145/3548732.3548737(25-32)Online publication date: 5-Aug-2022
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Efficient search for the top-k probable nearest neighbors in uncertain databases

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 1, Issue 1

August 2008

1216 pages

ISSN:2150-8097

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VLDB Endowment

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Published: 01 August 2008

Published in PVLDB Volume 1, Issue 1

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Cited By

Züfle A(2024)Probabilistic Counting in Uncertain Spatial Databases Using Generating FunctionsSpatial Gems, Volume 210.1145/3617291.3617300(85-96)Online publication date: 25-Jan-2024
(2024)Spatial Gems, Volume 2undefinedOnline publication date: 25-Jan-2024
Emrich TKriegel HZüfle AKröger PRenz M(2022)Complete and Sufficient Spatial Domination of Multidimensional RectanglesSpatial Gems, Volume 110.1145/3548732.3548737(25-32)Online publication date: 5-Aug-2022
Li HTang BLu HCheema MJensen CIves ZBonifati AEl Abbadi A(2022)Spatial Data Quality in the IoT Era: Management and ExploitationProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3522568(2474-2482)Online publication date: 10-Jun-2022
Li HLu HJensen CTang BCheema M(2022)Spatial Data Quality in the Internet of Things: Management, Exploitation, and ProspectsACM Computing Surveys10.1145/349833855:3(1-41)Online publication date: 3-Feb-2022
Bavi KLian X(2022)Range-constrained probabilistic mutual furthest neighbor queries in uncertain databasesKnowledge and Information Systems10.1007/s10115-022-01807-065:6(2375-2402)Online publication date: 24-Dec-2022
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Chen LLian X(2022)Query Processing over Uncertain DatabasesundefinedOnline publication date: 2-Mar-2022
Ding XSheng SLiu JZhou P(2021)Efficient Probabilistic K-NN Computation in Uncertain Sensor NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30998648:3(2575-2587)Online publication date: 1-Jul-2021
Dvir TPeres RRudnick Z(2020)Modelling the expected probability of correct assignment under uncertaintyScientific Reports10.1038/s41598-020-71558-x10:1Online publication date: 15-Sep-2020
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