CAREER: Enabling Distributed and In-Situ Analysis for Multidimensional Structured Data

职业：实现多维结构化数据的分布式和原位分析

• 批准号: 1453430
• 负责人: Trilce Estrada
• 金额: $ 41.3万
• 依托单位: University of New Mexico
• 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 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453430&HistoricalAwards=false
• 关键词: CAREER; Enabling; Distributed; Situ; Analysis

Advances in modern science have led to explosions of data across all science, technology, engineering, and mathematics (STEM) disciplines. Extracting meaningful knowledge from this large pool of information has become both complicated and costly. In fields like genomics and astronomy, where very large volumes of data are produced daily, it is necessary to store repositories throughout multiple, geographically distinct locations. This type of data allocation results in expensive computations and incomplete analyses. For health informatics and finances, data is typically isolated between research centers due to privacy, security, or cost issues. Again, the inability to have a global view of the data yields inaccurate outcomes at computation time. The classic centralized approach to analyzing data no longer produces optimal results; it has become a major bottleneck, hindering the advantages Big Data has to offer. Current solutions for distributed analysis still lack generality, scalability, or accuracy. This project aims to ameliorate problems in the management of distributed data while enabling scalable and accurate analyses. The project provides a comprehensive approach to handle data-to-knowledge extraction, representation, and learning at scale. Products of this research include: (1) an algorithmic suite of semantic projections and scalable learning methods for efficient data dimensionality reduction, pattern recognition, anomaly detection, and clustering, and (2) an open source middleware for coupling distributed data acquisition processes with in-situ analytics and crowd sourcing. These products will be made available through a GitHub repository at https://github.com/distributedreasoningatunm. Moreover, the crowd sourcing extension doubles as an educational platform, which aims to attract interest in the STEM fields.

现代科学的进步导致了所有科学，技术，工程和数学（STEM）学科的数据爆炸。从这些大量信息中提取有意义的知识既复杂又昂贵。在每天生产大量数据的基因组学和天文学等领域中，有必要在多个地理位置上不同的位置存储存储库。这种类型的数据分配导致昂贵的计算和不完整的分析。对于健康信息和财务，由于隐私，安全性或成本问题，研究中心之间通常在研究中心之间进行数据。同样，无法在计算时间内对数据的全局视图产生不准确的结果。分析数据的经典集中式方法不再产生最佳结果。它已成为一个主要的瓶颈，阻碍了大数据提供的优势。当前用于分布式分析的解决方案仍然缺乏通用性，可扩展性或准确性。 该项目旨在改善分布式数据管理的问题，同时实现可扩展和准确的分析。 该项目提供了一种全面的方法来按大规模处理数据对知识提取，表示和学习。这项研究的产品包括：（1）语义投影的算法套件和可扩展的学习方法，用于有效的数据维度降低，模式识别，异常检测和聚类，以及（2）与分布式数据采集过程的开源中间件，以及与SITU分析和人群中的分布式数据采集过程。这些产品将通过https://github.com/distributedReasoningatunm的GitHub存储库提供。 此外，人群采购的扩展是一个教育平台，旨在吸引对STEM领域的兴趣。