III-CTX: Collaborative Research: Spatio-Temporal Data Mining For Global Scale Eco-Climatic Data

III-CTX：协作研究：全球规模生态气候数据的时空数据挖掘

• 批准号: 0712987
• 负责人: Pang-Ning Tan
• 金额: --
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0712987&HistoricalAwards=false
• 关键词: III; CTX; Collaborative; Research; Spatio

ContextThe remote sensing data that consists of satellite observations of the land surface, biosphere, solid Earth, atmosphere, and oceans, combined with historical climate records and predictions from ecosystem models, offers new opportunities for understanding how the Earth is changing, for determining what factors cause these changes, and for predicting future changes. In turn, this could provide an unprecedented opportunity for predicting and preventing future ecological problems by managing the ecology and health of our planet. Data mining and knowledge discovery techniques can aid this effort by discovering patterns that capture complex interactions among ocean temperature, air pressure, surface meteorology, and terrestrial carbon flux. Intellectual MeritThe goals of this work are twofold: 1) to better understand global scale patterns in biosphere processes, particularly patterns in the global carbon cycle and climate system. More specifically, the proposed data mining research is driven by the need to address the following two challenges: (i) understanding how ocean, atmosphere and land processes are coupled and (ii) detecting and predicting ecosystem disturbances such as fires, floods, and hurricanes. 2) to support innovative Computer Science (CS) research in data mining. In particular, the spatio-temporal nature of Earth Science data means that standard CS data mining techniques often cannot be directly applied. As an example, in Earth Science research, a key step is the selection of the locations and time periods that are to be used to investigate possible relationships between two Earth Science phenomena, e.g., El Nino and milder winters in the Midwestern United States. Currently, this selection is based on domain knowledge, but automating this process would be very beneficial. The proposed work will develop new data mining techniques that address the high dimensionality, large size, and spatio-temporal nature of the data. Broader Impacts: New algorithms and techniques for the analysis of large spatio-temporal data sets developed in this project will be made available to other researchers within the Earth Science community. To a large extent, this project will encapsulate these algorithms within easy to use visual tools so that users will be able to more easily extract useful knowledge from Earth Science data sets. Although the focus will be on Earth Science data, the data mining techniques that are developed will be applicable to a wide variety of other fields that have data collected over time on a spatial grid. To give a specific example, spatio-temporal clustering has been used to track cyclones and animal migrations, and to model mobile phone users and neuronal activities in the brain, and the new spatio-temporal clustering techniques could also prove useful for these applications.

遥感数据包括对陆地表面、生物圈、固体地球、大气和海洋的卫星观测，结合历史气候记录和生态系统模型的预测，为了解地球如何变化、确定引起这些变化的因素以及预测未来变化提供了新的机会。反过来，这可以提供一个前所未有的机会，通过管理我们星球的生态和健康来预测和预防未来的生态问题。数据挖掘和知识发现技术可以通过发现捕获海洋温度，气压，表面气象学和陆地碳通量之间复杂相互作用的模式来帮助这一努力。知识价值这项工作的目标有两个：1）更好地了解生物圈过程的全球规模模式，特别是全球碳循环和气候系统的模式。更具体地说，拟议的数据挖掘研究是由解决以下两个挑战的需要驱动的：（i）了解海洋，大气和陆地过程是如何耦合的;（ii）检测和预测生态系统干扰，如火灾，洪水和飓风。 2)支持数据挖掘方面的创新计算机科学（CS）研究。特别是，地球科学数据的时空性质意味着标准的CS数据挖掘技术往往不能直接应用。例如，在地球科学研究中，一个关键步骤是选择用于调查两种地球科学现象之间可能关系的地点和时间段，例如，厄尔尼诺现象和美国中西部的暖冬。目前，这种选择是基于领域知识，但自动化这个过程将是非常有益的。拟议的工作将开发新的数据挖掘技术，解决高维，大尺寸和时空性质的数据。更广泛的影响：该项目开发的分析大型时空数据集的新算法和技术将提供给地球科学界的其他研究人员。在很大程度上，该项目将把这些算法封装在易于使用的可视化工具中，以便用户能够更容易地从地球科学数据集中提取有用的知识。虽然重点将是地球科学数据，开发的数据挖掘技术将适用于各种各样的其他领域，随着时间的推移收集的数据在一个空间网格。举一个具体的例子，时空聚类已被用于跟踪气旋和动物迁移，并模拟移动的手机用户和大脑中的神经元活动，新的时空聚类技术也可以证明对这些应用有用。