CIF21 DIBBs: EI: Vizier, Streamlined Data Curation

大数据有望对我们生活的许多方面产生积极影响，但收集数据来回答问题或导出预测模型可能具有挑战性。 数据科学家通常必须经过多轮的管理或“争论”，在这些过程中，数据被组织、精炼、清理和合并在一起，然后才能被分析。 策展通常是缓慢和昂贵的，但对于获得有用和值得信赖的答案至关重要。 该项目开发了一个名为Vizier的软件工具，旨在简化数据管理，并使没有计算机科学专业知识的领域专家能够管理自己的数据。 Vizier策展通过使广泛的用户能够提高数据质量来放大大数据的价值，并在这样做的过程中使政府，工业和科学中的许多类型的数据驱动工作受益。Vizier具有直观的界面，结合了笔记本和电子表格的元素，使分析师能够快速查看，编辑和修改数据。 此功能由自动化数据清理步骤框架补充，该框架与手动管理操作无缝集成。 Vizier的核心是一个用于管理策展工作流和数据的不确定性和来源的系统，使用户能够跟踪更高级别的策展操作以及跟踪数据的谱系。通过透明地维护所有用户操作的历史记录及其对策展数据的影响，Vizier实现了无遗憾的探索和策展，其中对数据的任何更改及其传递效果都可以撤销。通过从过去的策展历史中学习，该系统还将能够为用户提供与上下文相关的建议，以进行额外的策展操作。该奖项由高级网络基础设施部门颁发，并由NSF社会、行为和经济科学理事会（社会和经济科学部门）共同支持。

Big Data promises to have a positive impact on many aspects of our lives, but assembling the data to answer questions or derive predictive models can be challenging. Data scientists must typically go through multiple rounds of curation, or 'wrangling,' where data are organized, refined, cleaned up, and merged together before they can be analyzed. Curation is often slow and costly, but is essential for obtaining useful and trustworthy answers. This project develops a software tool called Vizier that aims to streamline data curation and enable domain experts who do not have computer science expertise to curate their own data. Easier curation magnifies the value of big data by enabling a wide range of users to improve data quality, and in doing so benefits numerous types of data-driven work in government, industry, and science.Vizier features an intuitive interface combining elements of notebooks and spreadsheets, allowing analysts to quickly see, edit, and revise data. This capability is complemented by a framework for automated data cleaning steps that are seamlessly integrated with manual curation operations. The heart of Vizier is a system for managing uncertainty and provenance of curation workflows and data, enabling the user to keep track of higher-level curation operations as well as track the lineage of data. By transparently maintaining the history of all the user's actions and their effect on the curated data, Vizier enables regret-free exploration and curation where any changes to the data and their transitive effects can be undone. By learning from past curation histories, the system will also be able to provide users with context-dependent recommendations for additional curation actions.This award by the Advanced Cyberinfrastructure Division is jointly supported by the NSF Directorate for Social, Behavioral and Economic Sciences (Division of Social and Economic Sciences).