Benchmarking Spatial Big Data
Benchmarking Spatial Big Data
复制标题
空间大数据基准测试
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
D. C. Cugler
中科院分区：
文献类型：
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作者：
S. Shekhar;Michael R. Evans;Viswanath Gunturi;Kwangsoo Yang;D. C. Cugler
越来越多的位置感知数据集的规模、种类和更新速度超过了空间计算技术的能力。本文讨论了这些数据集所带来的新挑战，我们称之为空间大数据SBD。SBD的例子包括手机和GPS设备的轨迹，车辆发动机测量，临时详细的道路地图等。SBD有潜力通过包括下一代路由服务在内的许多新技术来改变社会。然而，设想的基于sdd的服务对当前的空间计算技术提出了几个重大挑战。SBD放大了由开始位置和结束位置指定的传统路由查询的部分信息和模糊性的影响。此外，SBD挑战了使用特定数据集的单一算法适用于所有情况的假设。SBD来源的巨大多样性大大增加了解决方法的多样性。随着新的SBD的出现，更新的算法可能会出现，这就需要一个灵活的架构来快速集成新的数据集和相关的算法。为了量化这些新算法的性能，需要新的基准来关注这些空间大数据集，以确保不同技术之间的适当比较。
Increasingly, location-aware datasets are of a size, variety, and update rate that exceeds the capability of spatial computing technologies. This paper addresses the emerging challenges posed by such datasets, which we call Spatial Big Data SBD. SBD examples include trajectories of cell-phones and GPS devices, vehicle engine measurements, temporally detailed road maps, etc. SBD has the potential to transform society via a number of new technologies including next-generation routing services. However, the envisaged SBD-based services pose several significant challenges for current spatial computing techniques. SBD magnifies the impact of partial information and ambiguity of traditional routing queries specified by a start location and an end location. In addition, SBD challenges the assumption that a single algorithm utilizing a specific dataset is appropriate for all situations. The tremendous diversity of SBD sources substantially increases the diversity of solution methods. Newer algorithms may emerge as new SBD becomes available, creating the need for a flexible architecture to rapidly integrate new datasets and associated algorithms. To quantify the performance of these new algorithms, new benchmarks are needed that focus on these spatial big datasets to ensure proper comparisons across techniques.