Dynamic Feature Extraction and Data Mining for the Analysis of Turbulent Flows

这项研究的目标是开发一类新的数据分析方法。这将涉及对物理上重要的事件进行动态识别，选择性地存储数据。然后，特征提取算法将询问该数据库以产生额外的对象数据库，该对象数据库是物理上重要的时空对象轨迹的紧凑表示。这些数据库对象将被用作新的数据挖掘方法的输入，以发现对象之间的因果关系。这种方法将导致数据的有效存储、数据集中重要事件的可视化以及用于数据中对象之间关系的高级分析的方法。

The objective of the proposed reserch is to develop a new class of data analysis methods. This will involve on-the-fly identification of physically-important events selectively store the data. This database will then be interrogated by feature extraction algorithms to yield an additional object database, which is a compact representation of the physically important space-time object trajectories. These database objects will be used as input to novel data mining methods to discover causal relationships between the objects. This approach will result in efficient storage of data, visualization of the important events within the data set, and methods for the high-level analysis of relationships between objects in the data.