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.
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