Publication Type

Conference Proceeding Article

Version

Publisher’s Version

Publication Date

9-2006

Abstract

Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as °uctuations of edge weights. The ¯rst one maintains the query results by processing only updates that may invalidate the current NN sets. The second method follows the shared execution paradigm to reduce the processing time. In particular, it groups together the queries that fall in the path between two consecutive intersections in the network, and produces their results by monitoring the NN sets of these intersections. We experimentally verify the applicability of the proposed techniques to continuous monitoring of large data and query sets.

Keywords

information retrieval, nearest neighbors, data query, road networks, network monitoring, query optimization

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

VLDB 2006: Proceedings of the 32nd International Conference on Very Large Data Bases: September 12-15, 2006, COEX, Seoul, Korea

First Page

43

Last Page

54

ISBN

9781595933850

Publisher

VLDB Endowment

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://www.vldb.org/conf/2006/p43-mouratidis.pdf

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