Continuous nearest-neighbor queries with location uncertainty

Continuous nearest-neighbor queries with location uncertainty In this paper, we consider the problem of evaluating the continuous query of finding the \$\$k\$\$ k nearest objects with respect to a given point object \$\$O_{q}\$\$ O q among a set of \$\$n\$\$ n moving point-objects. The query returns a sequence of answer-pairs, namely pairs of the form \$\$(I,\, S)\$\$ ( I , S ) such that \$\$I\$\$ I is a time interval and \$\$S\$\$ S is the set of objects that are closest to \$\$O_{q}\$\$ O q during \$\$I\$\$ I . When there is uncertainty associated with the locations of the moving objects, \$\$S\$\$ S is the set of all the objects that are possibly the \$\$k\$\$ k nearest neighbors. We analyze the lower bound and the upper bound on the maximum number of answer-pairs, for the certain case and the uncertain case, respectively. Then, we consider two different types of algorithms. The first is off-line algorithms that compute a priori all the answer-pairs. The second type is on-line algorithms that at any time return the current answer-pair. We present algorithms for the certain case and the uncertain case, respectively, and analyze their complexity. We experimentally compare different algorithms using a database of 1 million objects derived from real-world GPS traces. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Continuous nearest-neighbor queries with location uncertainty

, Volume 24 (1) – Feb 1, 2015
26 pages

/lp/springer_journal/continuous-nearest-neighbor-queries-with-location-uncertainty-CPM4u9trd0
Publisher
Springer Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-014-0361-2
Publisher site
See Article on Publisher Site

Abstract

In this paper, we consider the problem of evaluating the continuous query of finding the \$\$k\$\$ k nearest objects with respect to a given point object \$\$O_{q}\$\$ O q among a set of \$\$n\$\$ n moving point-objects. The query returns a sequence of answer-pairs, namely pairs of the form \$\$(I,\, S)\$\$ ( I , S ) such that \$\$I\$\$ I is a time interval and \$\$S\$\$ S is the set of objects that are closest to \$\$O_{q}\$\$ O q during \$\$I\$\$ I . When there is uncertainty associated with the locations of the moving objects, \$\$S\$\$ S is the set of all the objects that are possibly the \$\$k\$\$ k nearest neighbors. We analyze the lower bound and the upper bound on the maximum number of answer-pairs, for the certain case and the uncertain case, respectively. Then, we consider two different types of algorithms. The first is off-line algorithms that compute a priori all the answer-pairs. The second type is on-line algorithms that at any time return the current answer-pair. We present algorithms for the certain case and the uncertain case, respectively, and analyze their complexity. We experimentally compare different algorithms using a database of 1 million objects derived from real-world GPS traces.

Journal

The VLDB JournalSpringer Journals

Published: Feb 1, 2015

References

• Modeling moving objects over multiple granularities
Hornsby, K; Egenhofer, M

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