The direction-constrained k nearest neighbor query Dealing with spatio-directional objects

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Finding k nearest neighbor objects in spatial databases is a fundamental problem in many geospatial systems and the direction is one of the key features of a spatial object. Moreover, the recent tremendous growth of sensor technologies in mobile devices produces an enormous amount of spatio-directional (i.e., spatially and directionally encoded) objects such as photos. Therefore, an efficient and proper utilization of the direction feature is a new challenge. Inspired by this issue and the traditional k nearest neighbor search problem, we devise a new type of query, called the direction-constrained k nearest neighbor (DCkNN) query. The DCkNN query finds k nearest neighbors from the location of the query such that the direction of each neighbor is in a certain range from the direction of the query. We develop a new index structure called MULTI, to efficiently answer the DCkNN query with two novel index access algorithms based on the cost analysis. Furthermore, our problem and solution can be generalized to deal with spatio-circulant dimensional (such as a direction and circulant periods of time such as an hour, a day, and a week) objects. Experimental results show that our proposed index structure and access algorithms outperform two adapted algorithms from existing kNN algorithms
Publisher
SPRINGER
Issue Date
2016-07
Language
English
Article Type
Article
Keywords

DIMENSIONAL SPACES; SEARCH; DATABASES

Citation

GEOINFORMATICA, v.20, no.3, pp.471 - 502

ISSN
1384-6175
DOI
10.1007/s10707-016-0245-2
URI
http://hdl.handle.net/10203/209468
Appears in Collection
CS-Journal Papers(저널논문)
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