Positions, Regions, and Clusters: Strata of Granularity in Location Modelling
Positions, Regions, and Clusters: Strata of Granularity in Location Modelling
复制标题
位置、区域和集群：位置建模中的粒度层
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
M. Beigl
中科院分区：
文献类型：
--
作者：
H. Schmidtke;M. Beigl
位置模型是泛在计算中使用的数据结构或知识库，用于表示和推理所谓的智能对象之间的空间关系，即日常对象，如杯子或建筑物，包含具有传感器和无线通信的计算设备。对象的位置在位置模型中，由区域、坐标位置或一组区域或位置表示。位置模型中的定性推理可以提高设备的智能，但由于表示格式之间的不兼容性而受到阻碍：拓扑推理适用于区域；定向推理，要定位；以及对聚类的集合隶属性的推理。我们提出了一个基于尺度空间的数学结构，为这三种类型的关系和表示提供了一个集成的语义。该结构反映了与位置建模相关的粒度和不确定性概念，并为rcc推理和基于投影的方向推理在位置模型中的应用提供了语义。
Location models are data structures or knowledge bases used in Ubiquitous Computing for representing and reasoning about spatial relationships between so-called smart objects, i.e. everyday objects, such as cups or buildings, containing computational devices with sensors and wireless communication. The location of an object is in a location model either represented by a region, by a coordinate position, or by a cluster of regions or positions. Qualitative reasoning in location models could advance intelligence of devices, but is impeded by incompatibilities between the representation formats: topological reasoning applies to regions; directional reasoning, to positions; and reasoning about set-membership, to clusters. We present a mathematical structure based on scale spaces giving an integrated semantics to all three types of relations and representations. The structure reflects concepts of granularity and uncertainty relevant for location modelling, and gives semantics to applications of RCC-reasoning and projection-based directional reasoning in location models.