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Adaptation of k-Nearest Neighbor Queries for Inter-building Environment

机译:适应k-最近邻居查询的建筑物间环境

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Nearest neighbor (kNN) is a spatial query where its main aim is to find k nearest object around a query point. This query has been widely used in outdoor environment to obtain point of interests in various GIS system, such as navigation and routing. The floor layout of a building can be represented with simple graph network. Unlike outdoor road network that usually only has single layer, an indoor network might have multiple layers which represents floors. In a multi-building area, buildings can be connected with the other buildings and create more complex network, which is called inter-building environment. In this paper, Dijkstra and Floyd Warshall algorithms as kNN algorithm are adapted and implemented in inter-building environment. Our experiments show that these algorithms are be able to adapt three dimensional graph for inter-building environment.
机译:最近邻居(kNN)是一种空间查询,其主要目的是在查询点附近找到k个最近的对象。该查询已广泛用于室外环境中,以获取各种GIS系统中的兴趣点,例如导航和路由。建筑物的楼层布局可以用简单的图形网络表示。与通常只有一层的室外道路网络不同,室内网络可能具有代表楼层的多层。在多建筑物区域中,建筑物可以与其他建筑物连接,并创建更复杂的网络,称为建筑物间环境。本文将Dijkstra和Floyd Warshall算法作为kNN算法,在建筑物间环境中进行了改编和实现。我们的实验表明,这些算法能够适应建筑物内部环境的三维图形。

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