ITR: Associative Mining of Large Datasets

ITR：大型数据集的关联挖掘

• 批准号: 0082634
• 负责人: Deborah Silver
• 金额: $ 47.4万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0082634&HistoricalAwards=false
• 关键词: ITR; Associative; Mining; Large; Datasets

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).

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的特征、模式和形状等内容来检查数据并检索所需的信息。与其他使用基于索引的坐标的方法不同，这种方法可以让科学家回答他们通常会问的各种问题，比如“我以前见过这种进化吗？”和“它与任何实验观察相似吗？”本项目开发的工具将在分布式时变数据上运行，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，用于对有趣的现象进行编目，并在运行时和存档数据中搜索有趣的现象。这项研究将特别针对两个领域，这两个领域具有其他科学领域的代表性，并且迫切需要科学的采矿工具：流体动力学（可压缩湍流的大规模、高精度直接数值模拟）和海洋学（模拟数据与热液羽流的声学观测的比较）。