CoTracks:一种基于多参数分割的日志数据跟踪有损压缩新模式

W. Balzano, M. D. Sorbo
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引用次数: 7

摘要

定位设备和服务的大规模扩散,传输和产生时空数据,引发了空间复杂性问题,并将研究重点转向高效和特定的算法来压缩这些大量存储或流动的数据。Co Tracks算法已被用于有损压缩GPS数据,利用它们所有时空特征之间的相似性。该算法的原始贡献在于考虑了轨道的高度,对三维数据的阐述和运动点的动态视觉,因为速度与时间紧密相连,是均匀性搜索中的重要参数之一。最小边界框是用来对数据点进行分组和生成近似轨迹关键点的工具。压缩比,也得到进一步的霍夫曼编码后,似乎有吸引力的高,表明这种新技术的新的有趣的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CoTracks: A New Lossy Compression Schema for Tracking Logs Data Based on Multiparametric Segmentation
A massive diffusion of positioning devices and services, transmitting and producing spatio-temporal data, raised space complexity problems and pulled the research focus toward efficient and specific algorithms to compress these huge amount of stored or flowing data. Co Tracks algorithm has been projected for a lossy compression of GPS data, exploiting analogies between all their spatio-temporal features. The original contribution of this algorithm is the consideration of the altitude of the track, an elaboration of 3D data and a dynamic vision of the moving point, because the speed, tightly linked to the time, is supposed to be one of the significant parameters in the uniformity search. Minimum Bounding Box has been the tool employed to group data points and to generate the key points of the approximated trajectory. The compression ratio, resulting also after a further Huffman coding, appears attractively high, suggesting new interesting developments of this new technique.
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