BIGDATA: Small: DA: Classification Platform for Novel Scientific Insight on Time-Series Data

BIGDATA：小型：DA：时间序列数据新科学见解的分类平台

• 批准号: 1251274
• 负责人: Joshua Bloom
• 金额: $ 73.35万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1251274&HistoricalAwards=false
• 关键词: BIGDATA; Small; DA; Classification; Platform

BIGDATA: Small: DA: Classification Platform for Novel Scientific Insight on Time-Series DataAbstractThe deepest insights into the nature of complex physical systems arise from the measurement of how observables of those systems change with time. Such dynamism - witnessed on scales ranging from atomic to Universal - reveals the underlying forces that govern the interaction of the constituents of those systems. The temporal sampling of data from sensors and from simulations, then, may be seen as a primary vector towards the deepest scientific insight. In this respect, mechanisms to quickly and robustly extract and mine knowledge from diverse time-series data can be fundamental tool of modern data-driven science. This project will build a webservice portal for scientific teams to train state-of-the-art machine-learning algorithms on existing data and receive autonomously generated classification statements on new data, whatever the scale. Massive data storage and the scaling/parallelism of computational algorithms (using commodity cloud services) will be abstracted from the end users. The envisioned framework will act both to simplify the algorithm selection and application processes as well as to educate the broad user base in modern machine-learning approaches. This project will lead to the implementation of novel and efficient feature extraction algorithms on irregularly sampled time-series data, and will make them available in the context of a robust and scalable platform integrated with classification and cross-validation, that will lead to informed use of the algorithms for reliable scientific insight. This learning and prediction platform will accelerate data-intensive decision-making, and will be a new data analytics tool for the autonomous discovery of knowledge across a diverse range of scientific disciplines. Geo-scientists may use it to find new robust earthquake trigger algorithms, enabling on-the-fly decision-making to improve emergency response times. Astronomers may rapidly detect anomalies, identifying a class of new variable stars buried within data from a time-domain imaging survey. Neuroscientists could incorporate improved real-time feedback and prediction into prosthetics control systems. As an intelligent agent, the platform could be used as an automated annotator for streaming biomedical data. This work will deliver a new open-source toolkit and web platform that can serve as a fundamental tool for time-domain science. By design, it will grow organically as user-contributed code is integrated into the platform. With burgeoning adoption among some data-driven science disciplines the webservice will emerge as an educational platform in the use of learning algorithms for time-series data and as a societal service that can be used by anyone (even outside of traditional scientific disciplines) to test hypotheses on large scales with minimal effort. The website will also act as a public repository for large, well-described datasets useful for validating new time-series classification and prediction algorithms. A series of short and semester-long courses will be developed (and broadly disseminated) to teach a new generation of scientists how to use the platform (and other widely available resources) as central 21st century research instruments.

大数据：小：DA：关于时间序列数据的新颖科学见解的分类平台摘要对复杂物理系统本质的最深刻见解源于对这些系统的可观测值如何随时间变化的测量。这种活力——在从原子到宇宙的尺度上都有所体现——揭示了控制这些系统组成部分相互作用的潜在力量。那么，来自传感器和模拟的数据的时间采样可以被视为实现最深入的科学洞察的主要向量。在这方面，从不同时间序列数据中快速、稳健地提取和挖掘知识的机制可以成为现代数据驱动科学的基本工具。 该项目将为科学团队构建一个网络服务门户，以在现有数据上训练最先进的机器学习算法，并接收针对新数据自动生成的分类语句（无论规模如何）。海量数据存储和计算算法的扩展/并行性（使用商品云服务）将从最终用户中抽象出来。设想的框架将简化算法选择和应用流程，并教育广大用户群了解现代机器学习方法。 该项目将导致在不规则采样的时间序列数据上实施新颖且高效的特征提取算法，并将其在集成分类和交叉验证的强大且可扩展的平台的背景下提供，这将导致知情使用可靠的科学见解的算法。这个学习和预测平台将加速数据密集型决策，并将成为跨不同科学学科自主发现知识的新数据分析工具。地球科学家可以用它来寻找新的强大的地震触发算法，从而实现即时决策，从而缩短紧急响应时间。天文学家可以快速检测异常现象，识别埋藏在时域成像调查数据中的一类新变星。神经科学家可以将改进的实时反馈和预测纳入假肢控制系统。作为智能代理，该平台可以用作流式生物医学数据的自动注释器。 这项工作将提供一个新的开源工具包和网络平台，可以作为时域科学的基本工具。按照设计，随着用户贡献的代码集成到平台中，它将有机增长。随着一些数据驱动的科学学科的迅速采用，网络服务将成为使用时间序列数据学习算法的教育平台，并成为任何人（甚至在传统科学学科之外）都可以使用来测试的社会服务以最小的努力进行大规模的假设。该网站还将充当大型、描述良好的数据集的公共存储库，可用于验证新的时间序列分类和预测算法。将开发（并广泛传播）一系列短期和学期课程，以教授新一代科学家如何使用该平台（和其他广泛可用的资源）作为 21 世纪的核心研究工具。