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; 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度以及望远镜，山脉，传感器和设备等大型工具的开发和部署提出了一个基本问题。 “可以以相同的指数率减少洞察力和知识发现的时间吗？”当前的答案显然是“否”的，因为结合了分析，采矿和发现大量数据集知识的关键步骤远远落后于软件，模拟和生成数据的进步。数据分析需要“数据驱动”计算和分析。这需要可扩展的软件，以减少数据，近似，分析，统计和自下而上发现。为了在科学发现中取得重大飞跃，需要进行大量数据来处理大量数据。该项目开发了创新，可扩展和可持续的数据分析算法，以启用有关高性能平行计算机的大量数据的分析和挖掘，其中包括（1）适用于时空数据，社区数据和检测图的图形，包括（1）适用于时空数据的自下而上和无监督的数据群集算法，适用于时空数据，以及不同的图形图。 （2）在时空数据中更改检测和异常检测； （3）在特定时间和空间约束中跟踪移动数据和群集动态。这些平行算法使用从科学应用中产生的大量数据，例如天体物理学，宇宙学模拟，气候建模和社交网络分析，对现代高性能平行计算机的结果验证和绩效评估。该项目直接解决空间数据分析的关键需求，以实现大量数据分析，绩效分析性分析，并确定大型分析。这项工作将通过加速科学发现来影响巨大的社会利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，可以在商业应用中使用和采用开发的软件基础架构，例如商业，社会，安全，药物发现等。源代码向公众开放，以供所有社区适应，建立，自定义和贡献，从而增加其价值和用法。