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Finding Visible kNN Objects in the Presence of Obstacles within the User’s View Field

1
NCTU Office of Research and Development, National Chiao Tung University, Hsinchu 300, Taiwan
2
MediaTek Inc., Hsinchu 300, Taiwan
3
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City 701, Taiwan
4
Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung City 807, Taiwan
*
Author to whom correspondence should be addressed.
The manuscript is an extended version of our previous report. We will explain the differences between this paper and the previous version in more detail on page 3.
ISPRS Int. J. Geo-Inf. 2019, 8(3), 151; https://doi.org/10.3390/ijgi8030151
Received: 8 February 2019 / Revised: 11 March 2019 / Accepted: 15 March 2019 / Published: 20 March 2019
(This article belongs to the Special Issue Spatial Databases: Design, Management, and Knowledge Discovery)

Abstract

In many spatial applications, users are only interested in data objects that are visible to them. Hence, finding visible data objects is an important operation in these real-world spatial applications. This study addressed a new type of spatial query, the View field-aware Visible k Nearest Neighbor (V2-kNN) query. Given the location of a user and his/her view field, a V2-kNN query finds data object p so that p is the nearest neighbor of and visible to the user, where visible means the data object is (1) not hidden by obstacles and (2) inside the view field of the user. Previous works on visible NN queries considered only one of these two factors, but not both. To the best of our knowledge, this work is the first to consider both the effect of obstacles and the restriction of the view field in finding the solutions. To support efficient processing of V2-kNN queries, a grid structure is used to index data objects and obstacles. Pruning heuristics are also designed so that only data objects and obstacles relevant to the final query result are accessed. A comprehensive experimental evaluation using both real and synthetic datasets is performed to verify the effectiveness of the proposed algorithms. View Full-Text
Keywords: view field; visible k nearest neighbor queries; index design; spatial databases; query processing algorithm view field; visible k nearest neighbor queries; index design; spatial databases; query processing algorithm
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Su, I.-F.; Chen, D.-L.; Lee, C.; Chung, Y.-C. Finding Visible kNN Objects in the Presence of Obstacles within the User’s View Field . ISPRS Int. J. Geo-Inf. 2019, 8, 151.

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