CRII: III: Spatio-Temporal Data Mining to Learn From Unlabeled Climate Data

CRII：III：从未标记的气候数据中学习的时空数据挖掘

• 批准号: 1555949
• 负责人: James Faghmous
• 金额: $ 19.1万
• 依托单位: Icahn School of Medicine at Mount Sinai
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1555949&HistoricalAwards=false
• 关键词: CRII; III; Spatio; Temporal; Data

Global climate change and its potential societal impacts have become one of our era's greatest challenges. Climate science is experiencing tremendous data growth (~100s of exabytes by 2020), yet our ability to learn from such data is limited because most of the existing data science methods are not well-suited for the noisy, heterogeneous, incomplete, and spatio-temporal data in climate science. This project will develop novel data mining methods to enhance our ability to learn from large and complex datasets with scientific and societal importance.Specifically, this project will focus on foundational advances to traditional pattern mining by incorporating information from multiple uncertain and incomplete datasets to discover statistically significant patterns in continuous data with an application to oceanography. We propose the notion of "spatio-temporal redundancy" where patterns are discovered in multiple related datasets to offset the lack of target labels in the data. This approach will address two major challenges in Big Data analysis for scientific discovery. First, as the size and complexity of the data grow, the likelihood of false discoveries increases significantly. Second the lack of ground truth verification data makes objective validation a challenge. Finally, we rely on First Principles and domain theory to constrain our data mining approaches to produce tractable and scientifically consistent results. These methods can generalize to various domains where no ground truth data are available but one has access to several information-rich, yet uncertain datasets.

全球气候变化及其潜在的社会影响已成为我们时代的最大挑战之一。气候科学正在经历巨大的数据增长（到2020年约为100艾字节），但我们从这些数据中学习的能力是有限的，因为大多数现有的数据科学方法都不适合气候科学中的噪声，异构，不完整和时空数据。该项目将开发新的数据挖掘方法，以提高我们从具有科学和社会重要性的大型复杂数据集中学习的能力。具体而言，该项目将侧重于传统模式挖掘的基础性进展，通过整合来自多个不确定和不完整数据集的信息，发现连续数据中具有统计意义的模式，并将其应用于海洋学。我们提出了“时空冗余”的概念，其中在多个相关数据集中发现模式，以弥补数据中缺乏目标标签。这种方法将解决科学发现大数据分析中的两个主要挑战。首先，随着数据规模和复杂性的增长，错误发现的可能性显著增加。第二，缺乏真实验证数据使客观验证成为一个挑战。最后，我们依靠第一原则和领域理论来约束我们的数据挖掘方法，以产生易于处理和科学一致的结果。这些方法可以推广到各种领域，在这些领域中没有地面实况数据，但可以访问几个信息丰富但不确定的数据集。