### Abstract

Nearest-neighbor (NN) search, which returns the nearest neighbor of a query point in a set of points, is an important and widely studied problem in many fields, and it has wide range of applications. In many of them, such as sensor databases, location-based services, face recognition, and mobile data, the location of data is imprecise. We therefore study nearest neighbor queries in a probabilistic framework in which the location of each input point is specified as a probability distribution function. We present efficient algorithms for (i) computing all points that are nearest neighbors of a query point with nonzero probability; (ii) estimating, within a specified additive error, the probability of a point being the nearest neighbor of a query point; (iii) using it to return the point that maximizes the probability being the nearest neighbor, or all the points with probabilities greater than some threshold to be the NN. We also present some experimental results to demonstrate the effectiveness of our approach.

Original language | English (US) |
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Title of host publication | PODS 2013 - Proceedings of the 32nd Symposium on Principles of Database Systems |

Pages | 115-126 |

Number of pages | 12 |

DOIs | |

State | Published - 2013 |

Event | 32nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2013 - New York, NY, United States Duration: Jun 22 2013 → Jun 27 2013 |

### Other

Other | 32nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2013 |
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Country | United States |

City | New York, NY |

Period | 6/22/13 → 6/27/13 |

### Fingerprint

### Keywords

- Approximate nearest neighbor
- Indexing uncertain data
- Probabilistic nearest neighbor
- Threshold queries

### ASJC Scopus subject areas

- Software
- Information Systems
- Hardware and Architecture

### Cite this

*PODS 2013 - Proceedings of the 32nd Symposium on Principles of Database Systems*(pp. 115-126) https://doi.org/10.1145/2463664.2465219

**Nearest neighbor searching under uncertainty II.** / Agarwal, Pankaj K.; Aronov, Boris; Har-Peled, Sariel; Phillips, Jeff M.; Yi, Ke; Zhang, Wuzhou.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*PODS 2013 - Proceedings of the 32nd Symposium on Principles of Database Systems.*pp. 115-126, 32nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2013, New York, NY, United States, 6/22/13. https://doi.org/10.1145/2463664.2465219

}

TY - GEN

T1 - Nearest neighbor searching under uncertainty II

AU - Agarwal, Pankaj K.

AU - Aronov, Boris

AU - Har-Peled, Sariel

AU - Phillips, Jeff M.

AU - Yi, Ke

AU - Zhang, Wuzhou

PY - 2013

Y1 - 2013

N2 - Nearest-neighbor (NN) search, which returns the nearest neighbor of a query point in a set of points, is an important and widely studied problem in many fields, and it has wide range of applications. In many of them, such as sensor databases, location-based services, face recognition, and mobile data, the location of data is imprecise. We therefore study nearest neighbor queries in a probabilistic framework in which the location of each input point is specified as a probability distribution function. We present efficient algorithms for (i) computing all points that are nearest neighbors of a query point with nonzero probability; (ii) estimating, within a specified additive error, the probability of a point being the nearest neighbor of a query point; (iii) using it to return the point that maximizes the probability being the nearest neighbor, or all the points with probabilities greater than some threshold to be the NN. We also present some experimental results to demonstrate the effectiveness of our approach.

AB - Nearest-neighbor (NN) search, which returns the nearest neighbor of a query point in a set of points, is an important and widely studied problem in many fields, and it has wide range of applications. In many of them, such as sensor databases, location-based services, face recognition, and mobile data, the location of data is imprecise. We therefore study nearest neighbor queries in a probabilistic framework in which the location of each input point is specified as a probability distribution function. We present efficient algorithms for (i) computing all points that are nearest neighbors of a query point with nonzero probability; (ii) estimating, within a specified additive error, the probability of a point being the nearest neighbor of a query point; (iii) using it to return the point that maximizes the probability being the nearest neighbor, or all the points with probabilities greater than some threshold to be the NN. We also present some experimental results to demonstrate the effectiveness of our approach.

KW - Approximate nearest neighbor

KW - Indexing uncertain data

KW - Probabilistic nearest neighbor

KW - Threshold queries

UR - http://www.scopus.com/inward/record.url?scp=84880555742&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880555742&partnerID=8YFLogxK

U2 - 10.1145/2463664.2465219

DO - 10.1145/2463664.2465219

M3 - Conference contribution

AN - SCOPUS:84880555742

SN - 9781450320665

SP - 115

EP - 126

BT - PODS 2013 - Proceedings of the 32nd Symposium on Principles of Database Systems

ER -