ITR: Associative Mining of Large Datasets

该项目的目标是开发一种方法和一套原型工具，以实现对非常大的科学数据集的“关联”挖掘。研究人员将使用解决方案特征、模式和形状等内容来检查数据并检索所需信息。与其他使用基于索引的坐标的方法不同，这让科学家们可以回答他们通常会问的问题--例如：我以前见过这种进化吗？以及“这与任何实验观察结果相似吗？”该项目开发的工具将对分布的时变数据进行操作，并将作为其他方法的模板。具体的技术目标包括开发分布式多分辨率技术，对感兴趣的现象进行编目，并在运行时和存档数据中搜索感兴趣的现象。这项研究将专门针对具有其他科学领域代表性和迫切需要科学采矿工具的两个领域：流体动力学(大规模、高精度可压缩湍流的直接数值模拟)和海洋学(模拟数据与热液羽流的声学观测数据的比较)。

The goal of this project is to develop a methodology and set of prototype tools to enable "associative" mining of very large scientific data sets. The researchers will use content, such as solution features, patterns, and shapes, to examine the data and retrieve required information. Unlike other approaches that use index-based coordinates, this lets scientists answer the kinds of questions that they typically ask - such as "Have I seen this evolution before?" and "Is it similar to any experimental observations?" The tools developed by this project will operate on distributed time-varying data and will act as templates for other methods. Specific technical objectives include developing distributed multi-resolution techniques for cataloging interesting phenomena and searching both run-time and archived data for interesting phenomena. The research will specifically target two domains that are representative of other scientific areas and have a pressing need for scientific mining tools: fluid dynamics (large-scale, high-accuracy Direct Numerical Simulation of compressible turbulence) and oceanography (comparison of simulation data with acoustic observations of hydrothermal plumes).