ITR: Collaborative Focused Mining of Atmospheric Aerosol Datasets: Integration of Mass Spectrometry and Environmental Monitoring

对大气颗粒物（气溶胶）在全球气候变化、人类健康和福祉以及地球生态系统中的作用的日益关注，产生了更好地了解大气污染物的组成、来源和影响的巨大需求。为了制定控制战略，以减轻气候变化的开始，以及环境和我们的生活质量的退化，有一个很大的需要更好地了解大气气溶胶的来源，动力学和成分。气溶胶科学的最新进展导致了新一代实时仪器的开发，这些仪器提供关于某些气溶胶特性的连续或半连续数据流。然而，这些仪器增加了大气气溶胶数据的复杂性，并显着增加了收集，管理和分析的数据量。该项目旨在挖掘大气气溶胶数据集，并在此过程中，开发可应用于其他领域的新型挖掘环境。时间序列分析，聚类和决策树是众所周知的技术，强大的软件是可用的，并将用于分析大气气溶胶数据集。此外，将酌情探索新的方法，包括一个称为子集挖掘的框架，该框架特别适合于发现不同数据集（部分）之间的相关性，如质谱和环境监测数据。一个重要的目标是开发一个框架，使用一种或多种挖掘技术创建多步分析，并通过将领域知识纳入分析来集中这些技术生成的模式。其目标是通过利用用户输入来自动探索大量替代模型，并利用并行探索多个模型所带来的优化，从而减少复杂分析所需的时间。一个相关的目标是使研究小组能够分享他们的结果，一直到数据集，以及如何在不断发展的数据集和复杂的分析链的分析环境中处理这些结果。该项目将在威斯康星大学麦迪逊分校和卡尔顿学院培训研究生和本科生，两个学科，计算机科学和大气化学。除了教育影响外，研究成果和工具将通过出版物和网络（www.cs.wisc.edu/admitr）广泛传播，预计将在两个方向上显著推进最新技术水平：（1）科学和监管工作，以了解和减轻空气污染和环境污染物的影响，（2）数据挖掘的基础，算法和技术。

Increasing concern over the role of atmospheric particles (aerosols) on global climate change, human health and welfare and the Earth's ecosystem has created a great need to better understand the composition, origin, and influence of atmospheric pollutants. In order to develop control strategies that can mitigate the onset of climate change, as well as the degradation of the environment and our quality of life, there is a great need to better understand the sources, dynamics, and compositions of atmospheric aerosols. Recent advances in aerosol science have led to the development of a new generation of real-time instruments, which provide continuous or semi-continuous streams of data about certain aerosol properties. However, these instruments have added a significant level of complexity to atmospheric aerosol data, and dramatically increased the amounts of data to be collected, managed, and analyzed. This project aims to mine atmospheric aerosol data sets, and in the process, to develop novel mining environments that can be applied in other domains as well. Time-series analysis, clustering, and decision trees are well-known techniques for which robust software is available, and will be used to analyze atmospheric aerosol datasets. In addition, new approaches will be explored as appropriate, including a framework called subset mining that is especially suited for finding correlations between (parts of) different datasets, such as mass spectrometry and environmental monitoring data. An important objective is to develop a framework to create multi-step analyses using one or more mining techniques, and to focus the patterns generated by these techniques by incorporating domain knowledge into the analysis. The goal is to reduce the time required for complex analyses by leveraging user input to automatically explore a large space of alternative models, and to take advantage of optimizations made possible by exploring several models in parallel. A related objective is to enable research groups to share their results, all the way down to the datasets and how they were processed to arrive at the results, in an analysis environment with continually evolving datasets and complex analysis chains. This project will train graduate and undergraduate students at UW-Madison and Carleton College in two disciplines, Computer Science and Atmospheric Chemistry. In addition to the educational impact, the research results and tools will be widely disseminated through publications and the Web (www.cs.wisc.edu/~raghu/admitr), and are expected to significantly advance the state of the art in two directions: (1) Scientific and regulatory efforts to understand and mitigate the impacts of air pollution and environmental contaminants, (2) Foundations, algorithms, and technology for data mining.