EarthCube Data Infrastructure: Intelligent Databases and Analysis Tools for Geospace Data

太阳活动和变化是决定地球状况的关键因素之一--S大气、全球趋势和气候变化。高能辐射和物质抛射形式的爆炸性事件在电离层和磁层引起地磁风暴，影响生物系统，扰乱电网和通信。为了了解和预测复杂和不断演变的地球系统，研究其与空间环境和太阳可变性的耦合是至关重要的。为了促进对太阳影响的跨学科研究，Pi和该团队将开发一个独特的数据环境，该环境将整合新的和存档的卫星和地面观测数据。综合数据环境将使研究人员能够有效地获取太阳和地球空间数据，并将其用于研究太阳活动和可变性的基本问题及其对地球系统的影响，以及开发新的预测能力。太阳物理学家和计算机科学家合作开发的建立智能综合数据库的创新跨学科方法将有助于在地球立方体和相关领域发现知识。拟议的活动将促进将创新的数据分析、数据可视化和数据驱动的建模技术转移到本科生和研究生一级的课堂教学以及可能受益于类似框架的其他研究领域。主要目标是开发可供地球科学界轻松使用的数据访问和分析工具，用于研究和模拟耦合地球系统的各种组成部分。该项目将开发新的工具来提取和分析现有的观测和建模数据，以便能够采用新的基于物理学和机器学习的方法来了解和预测太阳活动及其对地球空间和地球系统的影响。地球空间数据非常丰富：每天都会获得数TB的太阳和空间观测数据。从众多航天器和地面数据档案中寻找相关信息并加以利用是一项至关重要的任务，目前也是一项艰巨的任务。该项目的范围是开发和评估数据集成工具，以满足两种类型的太阳物理数据的共同数据访问和发现需求：1)长期天气活动和可变性，以及2)极端地球效应太阳事件。该项目将整合现有的数据资源，如太阳物理知识数据库、太阳动力学观测站联合科学业务中心、虚拟太阳观测站、太阳物理综合观测站等。该方法包括开发数据集成基础设施和访问方法，所述数据集成基础设施和访问方法能够1)自动搜索和识别由空间和地面观测站产生的图像模式和事件数据记录，2)并行多波长/多仪器数据库条目与唯一模式或事件识别符的自动关联，3)自动检索这种数据记录和管道处理，以便根据可从互补数据源推断的预定义的一组物理参数来注释每个模式或事件，以及4)生成能够提供快速搜索、快速预览和自动数据检索能力的模式或目录和相关联的用户友好的图形界面工具

Solar activity and variability are among the key factors determining the state of the Earth?s atmosphere, global trends and climate changes. Explosive events in the form of high-energy radiation and mass ejections cause geomagnetic storms in the ionosphere and magnetosphere, affecting biological systems, disrupting power grids, and communications. For understanding and predicting the complex and evolving Earth system, it is critical to investigate its coupling to the space environment and to solar variability. To facilitate interdisciplinary research on solar influences, PI and the team will develop a unique data environment that will integrate new and archived satellite and ground-based observational data. The integrated data environment will allow researchers to efficiently access solar and geospace data and use them for studying fundamental problems of solar activity and variability and their impacts on Earth systems, as well as for developing new predictive capabilities. The innovative interdisciplinary approach for building an intelligent integrated database, developed in collaboration between heliophysicists and computer scientists, will contribute to knowledge discovery in the EarthCube and associated fields. The proposed activities will facilitate the transfer of innovative data analysis, data visualization, and data-driven modeling techniques to in-class teaching at the undergraduate and graduate levels and to other fields of research that may benefit from a similar framework.The primary goal is to develop tools for data access and analysis that can be easily used by the Geoscience community for studying and modeling various components of the coupled Earth system. The project will develop innovative tools to extract and analyze the available observational and modeling data in order to enable new physics-based and machine-learning approaches for understanding and predicting solar activity and its influence on the geospace and Earth systems. The geospace data are abundant: several terabytes of solar and space observations are obtained every day. Finding the relevant information from numerous spacecraft and ground-based data archives and using it is a paramount, and currently a difficult task. The scope of the project is to develop and evaluate data integration tools to meet common data access and discovery needs for two types of Heliophysics data: 1) long-term synoptic activity and variability, and 2) extreme geoeffective solar events. The project will integrate existing data resources, such as the Heliophysics Knowledge Database (HEK), Solar Dynamics Observatory Joint Science Operations Center (SDO JSOC), Virtual Solar Observatory (VSO), Heliophysics Integrated Observatory (HELIO), and others. The methodology consists in the development of a data integration infrastructure and access methods capable of 1) automatic search and identification of image patterns and event data records produced by space and ground-based observatories, 2) automatic association of parallel multi-wavelength/multi-instrument database entries with unique pattern or event identifiers, 3) automatic retrieval of such data records and pipeline processing for the purpose of annotating each pattern or event according to a predefined set of physical parameters inferable from complimentary data sources, and 4) generation of a pattern or catalog and associated user-friendly graphical interface tools that are capable to provide fast search, quick preview, and automatic data retrieval capabilities