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

BigData：Small：DA：新型科学洞察时间序列数据分类平台摘要对复杂物理系统本质的最深刻见解来自对这些系统的可观性如何随时间变化的测量。这种能动性--从原子到宇宙--揭示了支配这些系统各组成部分相互作用的根本力量。因此，来自传感器和模拟的数据的时间采样可以被视为通向最深科学洞察的主要载体。在这方面，快速有力地从各种时间序列数据中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建立一个网络服务门户，以训练现有数据的最先进的机器学习算法，并接收关于新数据的自主生成的分类声明，无论规模如何。海量数据存储和计算算法的可伸缩性/并行性(使用商用云服务)将从最终用户手中抽象出来。设想的框架将既简化算法选择和应用程序，又用现代机器学习方法教育广大用户。该项目将导致对不规则抽样的时间序列数据实施新颖和有效的特征提取算法，并将使这些算法在一个结合了分类和交叉验证的强大和可扩展的平台中可用，从而在知情的情况下使用这些算法进行可靠的科学洞察。这一学习和预测平台将加速数据密集型决策，并将成为一种新的数据分析工具，用于自主发现各种科学学科的知识。地球科学家可能会利用它来寻找新的强大的地震触发算法，从而实现即时决策，以缩短应急响应时间。天文学家可能会迅速探测到异常，识别出一类新的变星，隐藏在来自时间域成像测量的数据中。神经学家可以将改进的实时反馈和预测纳入假肢控制系统。作为一个智能代理，该平台可以用作生物医学数据流的自动注释器。这项工作将提供一个新的开源工具包和网络平台，可以作为时间域科学的基本工具。根据设计，随着用户贡献的代码集成到平台中，它将有机地增长。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，以及任何人(甚至是传统科学学科以外的人)都可以使用的社会服务，以最小的努力在大范围内检验假设。该网站还将作为大型、描述良好的数据集的公共储存库，这些数据集有助于验证新的时间序列分类和预测算法。将开发(并广泛传播)一系列为期一学期的短期课程，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世纪的中心研究工具。

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.