Nearest-neighbor searching under uncertainty

Published

Journal Article

Nearest-neighbor queries, which ask for returning the nearest neighbor of a query point in a set of points, are important and widely studied in many fields because of a wide range of applications. In many of these applications, 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 and/or query point is specified as a probability density function and the goal is to return the point that minimizes the expected distance, which we refer to as the expected nearest neighbor (ENN). We present methods for computing an exact ENN or an ε-approximate ENN, for a given error parameter 0 < ε < 1, under dierent distance functions. These methods build an index of near-linear size and answer ENN queries in polylogarithmic or sublinear time, depending on the underlying function. As far as we know, these are the first nontrivial methods for answering exact or ε-approximate ENN queries with provable performance guarantees. © 2012 ACM.

Full Text

Duke Authors

Cited Authors

  • Agarwal, PK; Efrat, A; Sankararaman, S; Zhang, W

Published Date

  • June 27, 2012

Published In

  • Proceedings of the Acm Sigact Sigmod Sigart Symposium on Principles of Database Systems

Start / End Page

  • 225 - 236

Digital Object Identifier (DOI)

  • 10.1145/2213556.2213588

Citation Source

  • Scopus