Machine Learning Meets Big Spatial Data (Revised)
Machine Learning Meets Big Spatial Data (Revised)
复制标题
DOI：
10.1109/mdm52706.2021.00014
复制
发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Ibrahim Sabek;M. Mokbel
中科院分区：
文献类型：
--
作者：
Ibrahim Sabek;M. Mokbel
生成数据量的激增推动了可扩展机器学习解决方案的兴起，以有效地分析此类数据并从这些数据中提取有用的见解。与此同时，近年来，空间数据变得无处不在，例如GPS数据，其规模越来越大。大数据的应用跨越了广泛的兴趣，包括跟踪传染病、气候变化模拟、药物成瘾等。因此，主要的研究工作是通过提供对现有机器学习解决方案的空间扩展或从头开始构建新的解决方案来支持这些应用程序中的高效分析和智能。在这场90分钟的研讨会中，我们全面回顾了机器学习和大空间数据交叉领域的最新研究成果。我们涵盖了机器学习的三个主要领域的现有研究努力和挑战，即数据分析、深度学习和统计推理。我们还讨论了现有的端到端系统，并强调了该领域未来研究的开放问题和挑战。
The proliferation in amounts of generated data has propelled the rise of scalable machine learning solutions to efficiently analyze and extract useful insights from such data. Meanwhile, spatial data has become ubiquitous, e.g., GPS data, with increasingly sheer sizes in recent years. The applications of big spatial data span a wide spectrum of interests including tracking infectious disease, climate change simulation, drug addiction, among others. Consequently, major research efforts are exerted to support efficient analysis and intelligence inside these applications by either providing spatial extensions to existing machine learning solutions or building new solutions from scratch. In this 90-minutes seminar, we comprehensively review the state-of-the-art work in the intersection of machine learning and big spatial data. We cover existing research efforts and challenges in three major areas of machine learning, namely, data analysis, deep learning and statistical inference. We also discuss the existing end-to-end systems, and highlight open problems and challenges for future research in this area.