EarthCube Building Blocks: Collaborative Proposal: A Geo-Semantic Framework for Integrating Long-Tail Data and Models

EarthCube 构建模块：协作提案：集成长尾数据和模型的地理语义框架

• 批准号: 1440315
• 负责人: Praveen Kumar
• 金额: $ 64.71万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1440315&HistoricalAwards=false
• 关键词: EarthCube; Building; Blocks; Collaborative; Proposal

The project offers a unique and transformative approach to integrate existing and emerging long-tail model and data resources. Many challenges hinder the seamless integration of models with data. These challenges compel scientists to perform the integration process manually. The primary challenges are a consequence of the knowledge latency between model and data resources and others are derived from inadequate adoption and exploitation of information technologies. Knowledge latency challenges increase exponentially when a user aims to integrate long-tail data (data collected by individual researchers or small research groups) and long-tail models (models developed by individuals or small modeling communities).The goal of this research is to develop a framework rooted in semantic techniques and approaches to support ?long-tail? models and data integration. The vision is to develop a decentralized knowledge-based platform that can be easily adopted across geoscience communities comprising of individual and small group researchers. This project offers a unique and transformative approach to integrate existing and emerging long-tail model and data resources. The project will develop a knowledge framework to close the loop from models? queries back to data sources by first investigating the required concepts architecture for integrating two leading examples of long-tail resources in geoscience: Community Surface Dynamic Modeling System (CSDMS) and Sustainable Environment Actionable Data (SEAD). The project will also develop a context-based data model that provides an explicit interpretation of a metadata attribute. The researchers will capture the metadata concepts and semantic from various geo-informatics systems and provide tools for ensuring conceptual integration between the resources. Next, the project will develop a knowledge discovery tool that allows automated coupling of a model and data coming from different contributors. Finally, the project will provide a prototype physical implementation of the knowledge framework in CSDMS modeling framework to demonstrate how it can advance the seamless discovery, selection, and integration between models and data, and how to achieve dynamic reusability of resources across multiple Earth Science long-tail resources.

该项目提供了一种独特而变革性的方法来整合现有和新兴的长尾模型和数据资源。许多挑战阻碍了模型与数据的无缝集成。这些挑战迫使科学家手动执行整合过程。首要挑战是模型和数据资源之间知识潜伏期的结果，而其他挑战源于信息技术的采用和开发不足。当用户旨在整合长尾数据（由个别研究人员或小型研究小组收集的数据）和长尾模型（由个人或小型建模社区开发的模型）时，知识潜伏期挑战呈指数增长。该研究的目的是开发植根于语义技术和方法的框架？模型和数据集成。愿景是开发一个基于知识的分散平台，该平台可以在包括个人和小组研究人员组成的地球科学社区中轻松采用。 该项目提供了一种独特而变革的方法，可以整合现有和新兴的长尾模型和数据资源。该项目将开发一个知识框架以关闭模型的循环？查询回到数据源，首先研究所需的概念体系结构，以整合地球科学中的长尾资源的两个主要示例：社区表面动态建模系统（CSDM）和可持续的环境可行数据（SEAD）。该项目还将开发一个基于上下文的数据模型，该模型提供对元数据属性的明确解释。研究人员将从各种地理信息系统中捕获元数据概念和语义，并提供确保资源之间概念整合的工具。接下来，该项目将开发一个知识发现工具，该工具允许自动耦合来自不同贡献者的模型和数据。最后，该项目将在CSDMS建模框架中提供知识框架的原型物理实施，以证明它如何推动模型和数据之间的无缝发现，选择和集成，以及如何在多个地球科学长尾资源中实现资源的动态可重复使用。