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）资助的。大多数气候研究的目标是探测随时间的变化。大部分数据是以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，并且往往在同一时间被组织为单独的地理参考文件，这是数据收集方式的函数。对这些数据文件进行细分以检测选定区域的变化（气候数据分析的一个非常常见的目标）是非常耗时的。该项目的目标是设计和原型化一个过程，将时间序列数据处理为纯对象，从而能够对大量多模态冰冻圈数据进行以时间为中心的变化分析。这个项目会创造什么？数据棒?作为纯对象数据库中的时空数据结构。本项目不是将数据存储在固定时间和可变空间范围内，而是将数据存储在固定空间范围内，并具有可变时间成分，从而对数据进行时空查询和搜索优化。数据棒是一个逻辑对象，它集成了地球上一个点（像素/网格单元）随时间的所有已知信息。将时间引入离散像素后，可以用数据库中的项表示随时间的变化，而不是使用带有时间戳的空间网格。其结果是一个非常高效的存储和表示系统。对该数据库的查询可以跨越时间和空间同时跨越数百万个数据棒。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，直接快速有效地查询特征、模式和异常。最初的重点将是由国家冰雪数据中心（NSIDC）维护的网格数据，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岛的真实数据来证明，以回答以下问题：在什么时间间隔上，格陵兰岛的反照率变化最快？或者，2007年是反常的融冰年吗？是什么局部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，现有工具无法很好地提供服务。数据棒结构和新的搜索算法将测试该技术解决关键时空问题的可行性、速度和性能。就更广泛的影响而言，该项目开发的一系列技术可以改变科学研究人员在许多科学领域（如使用时空数据的生命科学和社会科学）访问和分析大量数据的方式。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、课程设计和（未来的）IT和科学专业人员（包括代表性不足的群体）的外展活动传播。学生将在网络密集、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.?