ITR/NGS: A Framework for Discovery, Exploration and Analysis of Evolutionary Simulation Data (DEAS)

ITR/NGS：进化模拟数据发现、探索和分析的框架 (DEAS)

• 批准号: 0326386
• 负责人: Raghu Machiraju
• 金额: $ 80.06万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0326386&HistoricalAwards=false
• 关键词: ITR; NGS; Framework; Discovery; Exploration

In science the challenge is always finding a signal in the noise. Examples include hurricane forecasting and monitoring both intelligence and seismic activity. Our proposal addresses these issues through a broad framework we call generalized feature mining. The framework has two major components: feature mining, and shape-based data mining and analysis. At its core, feature mining detects features for a specific application domain. Each instance involves a specific extended shape description tailored to it. For evolutionary simulations, feature mining also tracks features across multiple temporal scales. Shape-based data mining and analysis learn from the process. The aim is to correlate information from the extended shape descriptors with transient detection to find or refine spatio-temporal rules for the evolution of features. Environmental influences, such as walls, must be built into the rules so they are predictive.To close the loop, the detected features can be displayed as they are found or refined. The evolutionary rules predicted by our framework can lead to new science { not only understanding the underlying phenomena but also leading to computationally simpler models that encapsulate the essentials.

在科学中，挑战总是在噪音中找到信号。例如，飓风预报和监测情报和地震活动。 我们的建议通过一个广泛的框架来解决这些问题，我们称之为广义特征挖掘。该框架有两个主要组成部分：特征挖掘和基于形状的数据挖掘和分析。特征挖掘的核心是检测特定应用程序域的特征。 每个实例都包含一个为其定制的特定扩展形状描述。对于进化模拟，特征挖掘还跟踪多个时间尺度上的特征。基于形状的数据挖掘和分析从这个过程中学习。我们的目标是从扩展的形状描述符与瞬态检测相关的信息，以找到或完善时空规则的演变功能。环境影响（如墙壁）必须内置到规则中，以便它们具有预测性。要关闭循环，可以在发现或细化时显示检测到的特征。我们的框架预测的进化规则可以导致新的科学，不仅可以理解潜在的现象，还可以导致计算上更简单的模型，这些模型包含了基本要素。