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首页> 外文会议>18th ACM SIGSPATIAL international conference on advances in geographic information systems 2010 >Usability Analysis of Compression Algorithms for Position Data Streams
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Usability Analysis of Compression Algorithms for Position Data Streams

机译：位置数据流压缩算法的可用性分析

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With the increasing use of sensor technology, the compression of sensor data streams is getting more and more important to reduce both the costs of further processing as well as the data volume for persistent storage. A popular method for sensor data compression is to smooth the original measurement curve by an approximated curve, which is bounded by a given maximum error value. Measurement values from positioning systems like GPS are an interesting special case, because they consist of two spatial and one temporal dimension. Therefore various standard techniques for approximation calculations like regression or line simplification algorithms cannot be directly applied. In this paper, we portray our stream data management system NexusDS and an operator for compressing sensor data. For the operator, we implemented various compression algorithms for position data streams. We present the required adaptations and the different characteristics of the compression algorithms as well as the results of our evaluation experiments, and compare them with a map matching approach, specifically developed for position data.
机译：随着传感器技术的日益普及，传感器数据流的压缩对于降低进一步处理的成本以及持久存储的数据量越来越重要。传感器数据压缩的一种流行方法是通过近似曲线平滑原始测量曲线，该近似曲线以给定的最大误差值为界。来自定位系统（如GPS）的测量值是一个有趣的特殊情况，因为它们由两个空间和一个时间维度组成。因此，不能直接应用各种近似计算的标准技术，如回归或线简化算法。在本文中，我们描绘了流数据管理系统NexusDS和用于压缩传感器数据的操作员。对于运营商，我们为位置数据流实现了各种压缩算法。我们介绍了压缩算法所需的适应性和不同特征以及我们的评估实验的结果，并将它们与专门为位置数据开发的地图匹配方法进行了比较。

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