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

在科学领域，挑战总是在噪音中寻找信号。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和分析。在其核心，特征挖掘检测特定应用程序域的特征。每个实例都涉及为其量身定做的特定扩展形状描述。对于进化模拟，特征挖掘还可以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吸取了教训。其目的是将来自扩展形状描述符的信息与瞬变检测相关联，以发现或改进特征演化的时空规则。环境影响，如墙，必须内置到规则中，以便它们是可预测的。为了闭合循环，可以在发现或改进检测到的要素时显示它们。我们的框架预测的进化规则可以导致新的科学{不仅理解潜在的现象，而且还导致封装本质的计算更简单的模型。

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.