通过相关属性模式的可视化进行智慧城市数据分析
Smart City Data Analysis via Visualization of Correlated Attribute Patterns
摘要：
城市状况由各种各样的传感器监测，这些传感器测量温度和交通量等多个属性。传感器的相关性有助于准确分析和理解城市状况。相关属性模式（CAP）挖掘发现多个属性之间的相关性，从传感器集空间上彼此接近，并在其测量时间相关。在本文中，我们开发了一个可视化系统CAP挖掘和演示分析的智慧城市数据。我们的可视化系统支持通过地图上的传感器位置及其测量值的时间变化直观地了解采矿结果。在我们的演示场景中，我们提供了从中国和西班牙桑坦德收集的四个智慧城市数据集。我们证明了我们的系统有助于智能城市数据的交互式分析。
英文摘要：
Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute pattern (CAP) mining discovers correlations among multiple attributes from the sets of sensors spatially close to each other and temporally correlated in their measurements. In this paper, we develop a visualization system for CAP mining and demonstrate analysis of smart city data. Our visualization system supports an intuitive understanding of mining results via sensor locations on maps and temporal changes of their measurements. In our demonstration scenarios, we provide four smart city datasets collected from China and Santander, Spain. We demonstrate that our system helps interactive analysis of smart city data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
