并行空时核密度估计
Parallel Space-Time Kernel Density Estimation
摘要：
可用数据的指数增长增加了对交互式探索性分析的需求。无法再通过手工抓取和简单统计来理解数据集。在地理信息系统（GIS）中，数据集通常由在空间和时间上局部化的事件组成;并且可视化这样的数据集涉及构建事件发生地点的地图。我们专注于在本文中的事件，是本地化的三个维度（纬度，经度和时间），并计算可视化管道的第一步，时空核密度估计（STKDE），这是最昂贵的计算。从一个黄金标准的实现，我们展示了如何算法设计和工程，并行分解，调度可以应用到近实时计算的空时内核密度估计。我们在从传染病、社交媒体和鸟类学中提取的真实世界数据集上验证了我们的技术。
英文摘要：
The exponential growth of available data has increased the need for interactive exploratory analysis. Dataset can no longer be understood through manual crawling and simple statistics. In Geographical Information Systems (GIS), the dataset is often composed of events localized in space and time; and visualizing such a dataset involves building a map of where the events occurred. We focus in this paper on events that are localized among three dimensions (latitude, longitude, and time), and on computing the first step of the visualization pipeline, space-time kernel density estimation (STKDE), which is most computationally expensive. Starting from a gold standard implementation, we show how algorithm design and engineering, parallel decomposition, and scheduling can be applied to bring near real-time computing to space-time kernel density estimation. We validate our techniques on real world datasets extracted from infectious disease, social media, and ornithology.
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。
