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

在科学中，挑战总是在噪音中找到信号。例子包括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与分析。特征挖掘的核心是检测特定应用领域的特征。每个实例都包含为其量身定制的特定扩展形状描述。对于进化模拟，特征挖掘还可以跨多个时间尺度跟踪特征。基于形状的数据挖掘和分析学习过程。目的是将来自扩展形状描述符的信息与瞬态检测相关联，以发现或改进特征演化的时空规则。环境的影响，比如墙壁，必须被纳入规则中，这样它们才具有预测性。要关闭这个循环，可以在发现或改进检测到的特征时显示它们。由我们的框架所预测的进化规则，不仅能带来理解潜在现象的新科学，还能带来计算上更简单的模型，这些模型概括了本质。

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.