CDI-Type II: Real-time Classification of Massive Time-series Data Streams

CDI-Type II：海量时序数据流实时分类

• 批准号: 0941742
• 负责人: Joshua Bloom
• 金额: $ 157.36万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941742&HistoricalAwards=false
• 关键词: CDI; Type; II; Real; time

The collection of new data in any discipline does not, in general, lead to the creation of new knowledge. With the current data deluge, the human role in scientific discovery, traditionally so important, must now be partially fulfilled by powerful algorithms. However, current tools and technology start to break down when discovery and understanding, by the very nature of the science at hand, must happen quickly and in near real-time.New astronomical surveys coming online in the next few years, many observing the same regions of the sky repeatedly in time, will collect more data in the next decade than in all of human history so far. Opening up truly new vistas on the dynamic universe requires both rapid data processing and quick decisions about what available resources (e.g., telescopes) worldwide must be marshalled to study newly discovered phenomena. This necessitates an intelligent "real-time" machine-based decision or "classification" framework that should be able to deal with incomplete (and in some cases spurious) information.This project will produce a framework for extracting novel science from large amounts of data in an environment where the computational needs vastly outweigh the available facilities, and intelligent (as well as dynamic) resource allocation is required. New theory will be developed that will allow current machine learning paradigms to scale to large parallel computing environments. The core result is the production, for projects generating thousands of gigabytes of new data a night (such as the proposed Large Synoptic Survey Telescope), of probabilistic statements about the physical nature of astronomical events. Uncovering anomalous events that do not fit easily into a currently accepted classification taxonomy - events that may lead to completely new scientific discoveries - will be particularly emphasized in this work.Building these computational tools now with concrete scientific returns in mind will form the foundation for more rapid transformative applications in other fields with similar demands and constraints (high-frequency financial data, robotics, medical signal monitoring, geophysics, weather, and particle physics). This endeavor will also serve for years as a training ground for students and researchers across several departments and disciplines, and will broaden their scope towards a truly interdisciplinary education. By exposing students in the physical sciences to cutting-edge computer science and machine learning concepts, this project will provide a frame-work for computational thinking that will lead to future innovation.

一般来说，在任何学科中收集新数据都不会导致新知识的创造。在当前的数据洪流中，人类在科学发现中的作用在传统上非常重要，现在必须由强大的算法部分发挥作用。然而，当前的工具和技术开始崩溃，因为根据手头科学的本质，发现和理解必须迅速和接近实时地发生。未来几年，许多新的天文调查上线，许多人在时间上重复观测同一天空区域，在未来十年收集的数据将超过人类历史上迄今为止的所有数据。要在动态宇宙中开辟真正新的前景，既需要快速的数据处理，也需要迅速决定必须整合世界各地的哪些可用资源(例如望远镜)来研究新发现的现象。这就需要一个基于机器的智能“实时”决策或“分类”框架，它应该能够处理不完整(在某些情况下是虚假的)信息。这个项目将产生一个框架，用于在计算需求远远超过现有设施的环境中从大量数据中提取新的科学，并且需要智能(以及动态)资源分配。将开发新的理论，使当前的机器学习范例能够扩展到大型并行计算环境。其核心成果是为每晚产生几千兆字节新数据的项目(如拟议的大型天文观测望远镜)制作关于天文事件物理性质的概率陈述。在这项工作中，将特别强调发现不容易符合当前接受的分类分类的异常事件-可能导致全新科学发现的事件。现在考虑到具体的科学回报来构建这些计算工具，将为在具有类似需求和约束的其他领域(高频金融数据、机器人、医学信号监测、地球物理、天气和粒子物理)更快速的变革性应用奠定基础。这一努力还将在未来几年成为多个系和学科的学生和研究人员的培训基地，并将拓宽他们的范围，实现真正的跨学科教育。通过让物理科学的学生接触尖端的计算机科学和机器学习概念，这个项目将提供一个框架，用于计算思维，将导致未来的创新。