Local Temporal Compression for (Globally) Evolving Spatial Surfaces
Local Temporal Compression for (Globally) Evolving Spatial Surfaces
复制标题
DOI：
10.1007/978-3-030-37188-3_19
复制
发表时间：
2019-12
期刊：
影响因子：
--
通讯作者：
Xu Teng;Prabin Giri;M. Dwyer;Jidong Sun;Goce Trajcevski
中科院分区：
文献类型：
--
作者：
Xu Teng;Prabin Giri;M. Dwyer;Jidong Sun;Goce Trajcevski
物联网（IoT）范式的进步已经能够从多个领域生成大量数据，捕获各种感兴趣的物理和社会现象的演变。如此庞大的数据生成的后果之一是，它需要被存储、处理和查询--沿着的是以直观的方式呈现答案。已经提出了许多技术来减轻数据量对存储和处理开销的影响，沿着还有带宽消耗，其中最主要的是压缩。在本文中，我们考虑了多个地理上分散的数据源正在生成数据流的设置-但是，来自离散位置的值用于构建连续（随时间变化）表面的表示。我们已经使用了不同的压缩技术来减少每个位置的原始测量的大小，并且我们分析了压缩对近似对应于特定现象的形状的演变的质量的影响。具体来说，我们使用来自离散位置的数据来构建TIN（不规则三角网），该TIN随着每个位置的测量值的变化而随时间推移而变化。为了分析局部应用的不同压缩技术的全局影响，我们在原始数据TIN和压缩数据TIN之间使用了不同的表面距离函数。我们提供了详细的讨论，根据我们的实验观察相应的（压缩方法，距离函数）对。
The advances in the Internet of Things (IoT) paradigm have enabled generation of large volumes of data from multiple domains, capturing the evolution of various physical and social phenomena of interest. One of the consequences of such enormous data generation is that it needs to be stored, processed and queried – along with having the answers presented in an intuitive manner. A number of techniques have been proposed to alleviate the impact of the sheer volume of the data on the storage and processing overheads, along with bandwidth consumption – and, among them, the most dominant is compression. In this paper, we consider a setting in which multiple geographically dispersed data sources are generating data streams – however, the values from the discrete locations are used to construct a representation of continuous (time-evolving) surface. We have used different compression techniques to reduce the size of the raw measurements in each location, and we analyzed the impact of the compression on the quality of approximating the evolution of the shapes corresponding to a particular phenomenon. Specifically, we use the data from discrete locations to construct a TIN (triangulated irregular networks), which evolves over time as the measurements in each locations change. To analyze the global impact of the different compression techniques that are applied locally, we used different surface distance functions between raw-data TINs and compressed data TINs. We provide detailed discussions based on our experimental observations regarding the corresponding(compression method, distance function)pairs.