CDI-Type I: Data Rods: Enabling Time-Series Analysis of Massive Multi-Modality Cryospheric Data

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。大多数气候研究的目标是发现随着时间的推移而发生的变化。许多数据是空间排列的遥感数据或模型输出的形式。这些数据集非常庞大，往往一次被组织成单独的地理参照文件，这是数据收集方式的函数。将这些数据文件细分以用于选定区域的变化检测气候数据分析的一个非常常见的目标是耗时的。该项目的目标是设计和建立一个将时间序列数据作为纯对象处理的过程，以便能够对海量多模式冰冻圈数据进行以时间为中心的变化分析。这个项目将创造？数据棒？作为纯对象数据库中的时空数据构造。该项目不是在固定的时间和可变的空间范围存储数据，而是以可变的时间分量在固定的空间范围存储数据，因此数据将针对时空查询和搜索进行优化。数据棒是一个逻辑对象，它集成了关于地球上某个点(像素/网格单元)的所有已知信息。将时间引入离散像素允许由数据库中的项来表示随时间的变化，这与带时间戳的空间网格相反。结果是一个非常高效的存储和表示系统。对这个数据库的查询可以同时跨越数百万个数据棒，跨越时间和空间。该项目还将开发时间序列变化分析技术，利用数据棒的时空方面高效、直接和快速地查询特征、模式和异常情况。最初的重点将是由国家冰雪数据中心(NSIDC)维护的网格化数据，但它将扩展到其他遥感和点数据。最初的科学重点将是评估格陵兰冰盖对海平面上升的贡献。该方法将通过使用格陵兰的真实数据来回答以下问题来演示：格陵兰上空的反照率在什么时间间隔变化最快？或者，2007年是一个反常的融化年吗？是什么地方因素导致了这种异常？这些问题涉及庞大的数据集和统计查询，而现有工具对这些数据的处理能力很差。数据棒结构和新的搜索算法将测试这项技术解决关键时空问题的可行性、速度和性能。在更广泛的影响方面，该项目开发的一套技术可以改变科学研究人员访问和分析许多科学领域的海量数据的方式，例如使用时空数据的生命科学和社会科学。预期的科学成果和技术将通过期刊文章、标准机构、网络学习资源、课程设计以及面向(未来)信息技术和科学专业人员(包括代表性不足的群体)的外联活动传播。学生将在网络密集型、跨学科的环境中接受培训。CDI主题：？从数据到知识？？理解复杂性。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The goal of most climate research is to detect change over time. Much of the data are in the form of spatiallyarranged remote sensing data or model output. These data sets are voluminous and tend to be organized as separate geo-referenced files for a single time, which is a function of how the data were collected. Subsetting these data files for change detection over select regions a very common goal of climate data analysis is time consuming. The goal of this project is to design and prototype a process for addressing time-series data as pure objects that will enable time-centric change analysis of massive multi-modality cryospheric data. This project will create ?Data Rods? as a spatiotemporal data construct in a pure object database. Rather than storing data at a fixed time and a variable spatial extent, this project will store the data at fixed spatial extents with a variable time component, thus the data will be optimized for space-time queries and searches. A Data Rod is a logical object that integrates all the information that is known about a point on the earth (the pixel/grid cell) through time. The introduction of time to the discrete pixel allows changes over time to be represented by items in the database, as opposed to timestamped spatial grids. The result is a very efficient storage and representation system. Queries to this database can span millions of data rods simultaneously across both time and space. This project will also develop time-series change analysis techniques, which exploit the spatiotemporal aspects of the Data Rods to efficiently query for features, patterns and anomalies directly and quickly. The initial focus will be on gridded data maintained by the National Snow and Ice Data Center (NSIDC), but it will be extended to other remote sensing and point data. The initial science focus will be to evaluate the contribution of the Greenland ice sheet to sea level rise. The approach will be demonstrated by using real data for Greenland to answer questions such as: At what time interval did the albedo over Greenland change the fastest? Or, was 2007 an anomalous melt year? What local factors contributed to this anomaly? These questions involve huge data sets and statistical queries that are poorly served by existing tools. The Data Rod construct and new search algorithms will be tested for feasibility, speed and performance of this technology to solve critical space-time questions. In terms of broader impacts, the set of technologies developed in this project could transform how science researchers access and analysis massive volumes of data in many scientific domains such as life and social science working with spatiotemporal data. The expected science results and techniques will be disseminated through journal articles, standards bodies, cyberlearning resources, curriculum design, and outreach activities to (future) IT and science professionals including underrepresented groups. Students will be trained in a cyber-intensive, interdisciplinary setting. CDI themes: ?From Data to Knowledge?, ?Understanding Complexity.?