Utilizing Cloud Computing to address big geospatial data challenges
Utilizing Cloud Computing to address big geospatial data challenges
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DOI：
10.1016/j.compenvurbsys.2016.10.010
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Chaowei Yang;Manzhu Yu;F. Hu;Yongyao Jiang;Yun Li
中科院分区：
文献类型：
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作者：
Chaowei Yang;Manzhu Yu;F. Hu;Yongyao Jiang;Yun Li
大数据的出现为研究、开发、创新和商业带来了新的机遇。它的特点是所谓的四个V：体积，速度，准确性和多样性，并可能通过处理大数据带来重大价值。大数据的4V向第五（价值）的转变，是对处理能力的巨大挑战。云计算已经作为一种新的范例出现，以提供计算作为用于解决不同处理需求的实用服务，具有a）按需服务、B）池化资源、c）弹性、d）宽带接入和e）测量服务。提供计算能力的实用性为大数据的4V向第五（价值）的转变提供了一种潜在的解决方案。本文探讨了如何利用云计算来应对大数据挑战，以实现这种转型。我们介绍和审查四个地理空间科学的例子，包括气候研究，地理空间知识挖掘，土地覆盖模拟和沙尘暴建模。该方法在表格框架中呈现，作为利用云计算大数据解决方案的指导。通过4个实例说明了框架方法对大数据处理生命周期的支持，包括管理、访问、挖掘分析、模拟和预测。这个表格框架也可以作为指导，为其他大地理空间数据挑战和倡议（如智慧城市）制定潜在的解决方案。
Big Data has emerged with new opportunities for research, development, innovation and business. It is characterized by the so-called four Vs: volume, velocity, veracity and variety and may bring significant value through the processing of Big Data. The transformation of Big Data's 4 Vs into the 5th (value) is a grand challenge for processing capacity. Cloud Computing has emerged as a new paradigm to provide computing as a utility service for addressing different processing needs with a) on demand services, b) pooled resources, c) elasticity, d) broad band access and e) measured services. The utility of delivering computing capability fosters a potential solution for the transformation of Big Data's 4 Vs into the 5th (value). This paper investigates how Cloud Computing can be utilized to address Big Data challenges to enable such transformation. We introduce and review four geospatial scientific examples, including climate studies, geospatial knowledge mining, land cover simulation, and dust storm modelling. The method is presented in a tabular framework as a guidance to leverage Cloud Computing for Big Data solutions. It is demostrated throught the four examples that the framework method supports the life cycle of Big Data processing, including management, access, mining analytics, simulation and forecasting. This tabular framework can also be referred as a guidance to develop potential solutions for other big geospatial data challenges and initiatives, such as smart cities.