GOALI: Data Mining Tools for Geospatial Databases--Enabling Technologies for the Environment

GOALI：地理空间数据库的数据挖掘工具——环境支持技术

• 批准号: 9820841
• 负责人: Margaret Dunham
• 金额: $ 14.1万
• 依托单位: Southern Methodist University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-10-01 至 2003-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9820841&HistoricalAwards=false
• 关键词: GOALI; Data; Mining; Tools; Geospatial

This project develops data mining algorithms, both association rules and prediction, targeted to geospatial data. This GOALI research is performed in collaboration with the SIVAM project underway at Raytheon Systems Company. Raytheon Systems Company, Garland Division, has the responsibility for the hardware and software development of SIVAM (System for the Vigilance of the Amazon). SIVAM's objective is to implement a surveillance and analysis infrastructure, including a very large (multi terabyte) geospatial database and associated visualization tools that will provide the Brazilian Government with the necessary information for the protection and sustainable development of the Amazon region. This research focuses on an important aspect of the overall problem: development of new algorithms for a specific target application. The developed algorithms scale to the massive amounts of data present as well as adapt to the available amount of main memory. In the SIVAM project, data is obtained on an ongoing basis (as time advances). The prediction algorithms are used to predict environmental catastrophes (such as flooding or deforestation) and are incremental in nature. State information is kept which "remembers" previous environmental data collected. As new data arrives, the state is advanced based on the data found. In addition, these structures used to save this state information are modified as learning takes place. The results of this research will advance the field of data mining and provide enabling technologies for the environment research and management.http://www.seas.smu.edu/~mhd/dm.html

该项目开发了针对地理空间数据的数据挖掘算法，包括关联规则和预测。这项目标研究是与雷神系统公司正在进行的Sivam项目合作进行的。雷神系统公司，加兰区，负责SIVAM(亚马逊警戒系统)的硬件和软件开发。SIVAM的目标是建立一个监测和分析基础设施，包括一个非常大的(数兆兆字节)地理空间数据库和相关的可视化工具，为巴西政府提供亚马逊地区保护和可持续发展所需的信息。这项研究集中在整个问题的一个重要方面：为特定的目标应用开发新的算法。开发的算法可扩展到存在的海量数据，并适应可用主存的数量。在SIVAM项目中，数据是持续获得的(随着时间的推移)。预测算法用于预测环境灾难(如洪水或砍伐森林)，本质上是递增的。状态信息被保存下来，这些信息“记住”以前收集的环境数据。当新数据到达时，根据找到的数据推进状态。此外，用于保存该状态信息的这些结构随着学习的进行而被修改。本研究的成果将推动数据挖掘领域的发展，为环境研究和management.http://www.seas.smu.edu/~mhd/dm.html提供技术支持