CI-EN: Enhancing and Supporting a Community-Based Data Analysis, Visualization, and Provenance Platform

CI-EN：增强和支持基于社区的数据分析、可视化和来源平台

• 批准号: 1405927
• 负责人: Juliana Freire
• 金额: $ 50万
• 依托单位: New York 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: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405927&HistoricalAwards=false
• 关键词: CI; EN; Enhancing; Supporting; Community

Increasingly, scientific breakthroughs are being enabled by advanced computing capabilities that allow researchers to explore growing volumes of digital data. Unfortunately, the infrastructure to design and conduct computational experiments has not kept pace with our ability to gather and generate data, leading to an unprecedented situation: data exploration is now the bottleneck to discovery. In this project, we will build infrastructure to streamline data exploration that supports a wide array of modern computational resources and tools, as well as enable transparency and reproducibility of scientific results.This project builds upon the successful VisTrails open-source system. VisTrails provides unique support for data-intensive research, a comprehensive provenance infrastructure, and a user-centered design. The system has been applied to a broad range of disciplines, including environmental science, physics, and bioinformatics, both for research and educational efforts. With a view towards expanding the impact of the VisTrails system to the computer science community, we will extend it in three significant directions, notably: add support for multi-threaded and parallel execution and programming frameworks such as Mapreduce; improve the extensibility of the system through support for Java libraries as well as flexible mechanisms for scripting; and design new infrastructure to simplify the creation and packaging of reproducible experiments. These improvements will enable new research opportunities by providing a platform for computationally-demanding experiments and supporting for large-scale data analysis, as well as for provenance research. In addition, we hope it will contribute to a broader adoption of verified reproducibility in computer science publications.

越来越多的科学突破是由先进的计算能力推动的，这些能力使研究人员能够探索越来越多的数字数据。不幸的是，设计和进行计算实验的基础设施没有跟上我们收集和生成数据的能力，导致了前所未有的情况：数据探索现在是发现的瓶颈。在这个项目中，我们将建立基础设施，以简化数据探索，支持广泛的现代计算资源和工具，并使科学结果具有透明度和重复性。该项目建立在成功的VisTrails开源系统的基础上。VisTrails为数据密集型研究、全面的来源基础设施和以用户为中心的设计提供独特的支持。该系统已被广泛应用于多个学科，包括环境科学、物理和生物信息学，用于研究和教育工作。为了将VisTrails系统的影响扩大到计算机科学界，我们将向三个重要方向扩展，特别是：增加对多线程和并行执行和编程框架的支持，如MapReduce；通过支持Java库和灵活的脚本编写机制来提高系统的可扩展性；以及设计新的基础设施，以简化可重复实验的创建和打包。这些改进将提供新的研究机会，为计算要求高的实验提供平台，并支持大规模数据分析以及来源研究。此外，我们希望它将有助于在计算机科学出版物中更广泛地采用经过验证的再现性。