SHF: Medium: Collaborative Research: Scalable Algorithms for Spatio-temporal Data Analysis

SHF：中：协作研究：时空数据分析的可扩展算法

• 批准号: 1409601
• 负责人: Alok Choudhary
• 金额: $ 70.93万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1409601&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Scalable

Acceleration of computing power of supercomputers along with development and deployment of large instruments such as telescopes, colliders, sensors and devices raises one fundamental question. "Can the time to insight and knowledge discovery be reduced at the same exponential rate?" The answer currently is clearly "NO", because a critical step that combines analytics, mining and discovering knowledge from the massive datasets has lagged far behind advances in software, simulation and generation of data. Analysis of data requires "data-driven" computing and analytics. This entails scalable software for data reduction, approximations, analysis, statistics, and bottom-up discovery. Scalable and parallel analytics software for processing large amount of data is required in order to make a significant leap forward in scientific discoveries. This project develops innovative, scalable, and sustainable data analytics algorithms to enable analysis and mining of massive data on high-performance parallel computers, which include (1) bottom-up and unsupervised data clustering algorithms that are suitable for spatio-temporal data, massive graph analytics, community computations, and detection of patterns in time-varying graphs, different types of data, and different data characteristics; (2) change detection and anomaly detection in spatio-temporal data; and (3) tracking moving data and cluster dynamics within certain time and space constraints. These parallel algorithms use the massive amount of data generated from scientific applications, such as astrophysics, cosmology simulations, climate modeling, and social networking analysis, for result verification and performance evaluation on modern high-performance parallel computers.This project directly addresses the critical needs for spatio-temporal data analysis, performance scalability, and programming productivity of large-scale scientific discovery via parallel analytics software for big data. This work will impact applications of enormous societal benefits and scientific importance such as climate understanding, environmental sustainability, astrophysics, biology and medicine by accelerating scientific discoveries. Furthermore, the developed software infrastructure can be used and adopted in commercial applications, such as commerce, social, security, drug discovery, and so on. The source codes are open to the public for all community to adapt, build-upon, customize and contribute to, thereby multiplying its value and usage.

超级计算机计算能力的加速以及望远镜、对撞机、传感器和设备等大型仪器的开发和部署提出了一个基本问题。 “洞察和发现知识的时间能否以同样的指数速度减少？”目前的答案显然是否定的，因为从海量数据集中分析、挖掘和发现知识的关键步骤已经远远落后于软件、模拟和数据生成的进步。数据分析需要“数据驱动”的计算和分析。这需要用于数据缩减、近似、分析、统计和自下而上发现的可扩展软件。为了在科学发现方面取得重大飞跃，需要用于处理大量数据的可扩展和并行分析软件。该项目开发创新、可扩展和可持续的数据分析算法，以实现在高性能并行计算机上分析和挖掘海量数据，其中包括（1）适用于时空数据、海量数据的自下而上和无监督数据聚类算法。图分析、社区计算以及时变图中的模式检测、不同类型的数据和不同的数据特​​征； (2)时空数据的变化检测和异常检测； (3)在一定的时间和空间限制内跟踪移动数据和集群动态。这些并行算法利用天体物理学、宇宙学模拟、气候建模和社交网络分析等科学应用产生的大量数据，在现代高性能并行计算机上进行结果验证和性能评估。该项目直接解决了关键需求通过大数据并行分析软件实现大规模科学发现的时空数据分析、性能可扩展性和编程效率。这项工作将通过加速科学发现来影响具有巨大社会效益和科学重要性的应用，例如气候理解、环境可持续性、天体物理学、生物学和医学。此外，开发的软件基础设施可以在商业应用中使用和采用，例如商业、社交、安全、药物发现等。源代码向公众开放，供所有社区适应、构建、定制和贡献，从而倍增其价值和用途。