TY - JOUR
T1 - Moving view field nearest neighbor queries
AU - Kim, Wooil
AU - Shim, Changbeom
AU - Heo, Wan
AU - Yi, Sungmin
AU - Chung, Yon Dohn
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. NRF-2017R1A2A2A05069318 ).
Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. NRF-2017R1A2A2A05069318).
Publisher Copyright:
© 2018 Elsevier B.V.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/1
Y1 - 2019/1
N2 - In this paper, we introduce a novel query type, the moving view field nearest neighbor (MVFNN) query —a continuous version of the view field nearest neighbor (VFNN) query. This query continuously retrieves the nearest object in the query's view field taking into account the changes of the query location and view field. In order to improve the performance of the query processing, we propose the notion of geographical and angular safe boundaries. We can skip redundant computation if the moved query satisfies the geographical and angular safe boundaries. Our method is easily applicable to existing services since we do not transform the general index structures. We prove the efficiency of our method by a series of experiments varying the parameters such as query's moving speed, view field angle, and the distribution of data objects.
AB - In this paper, we introduce a novel query type, the moving view field nearest neighbor (MVFNN) query —a continuous version of the view field nearest neighbor (VFNN) query. This query continuously retrieves the nearest object in the query's view field taking into account the changes of the query location and view field. In order to improve the performance of the query processing, we propose the notion of geographical and angular safe boundaries. We can skip redundant computation if the moved query satisfies the geographical and angular safe boundaries. Our method is easily applicable to existing services since we do not transform the general index structures. We prove the efficiency of our method by a series of experiments varying the parameters such as query's moving speed, view field angle, and the distribution of data objects.
KW - Augmented reality
KW - Continuous query
KW - Location-based service
KW - Moving view field nearest neighbor query
KW - Spatial databases
UR - http://www.scopus.com/inward/record.url?scp=85058820286&partnerID=8YFLogxK
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U2 - 10.1016/j.datak.2018.12.002
DO - 10.1016/j.datak.2018.12.002
M3 - Article
AN - SCOPUS:85058820286
VL - 119
SP - 58
EP - 70
JO - Data and Knowledge Engineering
JF - Data and Knowledge Engineering
SN - 0169-023X
ER -