Authors:
George Roumelis
1
;
Michael Vassilakopoulos
2
;
Antonio Corral
3
and
Yannis Manolopoulos
1
Affiliations:
1
Aristotle University, Greece
;
2
University of Thessaly, Greece
;
3
University of Almeria, Spain
Keyword(s):
Spatial Query Processing, Plane-Sweep, Group Nearest-Neighbor Query, Algorithms.
Related
Ontology
Subjects/Areas/Topics:
Computational Geometry
;
Computer Vision, Visualization and Computer Graphics
;
Data Engineering
;
Databases and Data Security
;
Image Formation and Preprocessing
;
Query Processing and Optimization
Abstract:
One of the most representative and studied queries in Spatial Databases is the (K) Nearest-Neighbor (NNQ),
that discovers the (K) nearest neighbor(s) to a query point. An extension that is important for practical applications
is the (K) Group Nearest Neighbor Query (GNNQ), that discovers the (K) nearest neighbor(s) to a
group of query points (considering the sum of distances to all the members of the query group). This query
has been studied during the recent years, considering data sets indexed by efficient spatial data structures. We
study (K) GNNQs, considering non-indexed data sets, since this case is frequent in practical applications. And
we present two (RAM-based) Plane-Sweep algorithms, that apply optimizations emerging from the geometric
properties of the problem. By extensive experimentation, using real and synthetic data sets, we highlight the
most efficient algorithm.