SI2: Conceptualization: Dynamic Languages for Scalable Data Analytics

这项计划拨款收集了科学界对一组称为可伸缩数据分析的功能的要求。该项目通过提供现有硬件资源、数据源和存储库以及系统软件基础设施之间的有效接口，调查支持科学发现的社区需求。拟议的工作重点是为工作科学家提供用于数据获取、管理、可视化、共享和分析的软件环境和工具，这些环境和工具可以扩展到大规模并行和云结构，但关键是，可以同样轻松地缩减到单个笔记本电脑。用于数据分析的软件系统是科学创新结构不可或缺的组成部分。获取、处理和分析大量复杂的结构化和非结构化数据的能力是不同学科的核心。虽然科学家可以利用多年来优化和改进的大型软件工具存储库，但科学数据集的快速演变特征带来了重大的新挑战。这些挑战通过软件系统来解决，这些软件系统能够增量地开发新软件、修改现有方法或集成现成组件的管道技术。为了满足这样的应用需求，科学家们越来越依赖动态的计算机编程语言。这些语言促进了交互式原型开发，支持快速开发，并且可以嵌入或用于管理复杂的科学软件管道。

This planning grant gathers scientific community requirements for a set of capabilities termed Scalable Data Analytics. The project investigates community needs to support scientific discovery by providing an effective interface between extant hardware resources, data sources and repositories, and system software infrastructure. The proposed effort focuses on software environments and tools for data acquisition, management, visualization, sharing, and analysis for the working scientist, which can scale up to massively parallel and cloud fabrics, but, crucially, which can as easily scale down to a single laptop.Software systems for data analytics are integral to the fabric of scientific innovation. The ability to acquire, process, and analyze large amounts of complex structured and unstructured data is at the core of diverse disciplines. While scientists can exploit large repositories of software tools optimized and refined over the years, significant new challenges are posed by the rapidly evolving characteristics of scientific datasets. These challenges are addressed by software systems that enable development of new software incrementally, modification of existing methods, or techniques for integrating pipelines of off-the-shelf components. For such application needs, scientists increasingly rely on dynamic computer programming languages. These languages facilitate interactive prototyping, support rapid development, and can be embedded or used to manage complex scientific software pipelines.