CIF21 DIBBs: EI: Vizier, Streamlined Data Curation

CIF21 DIBB：EI：Vizier，简化的数据管理

• 批准号: 1640864
• 负责人: Oliver Kennedy
• 金额: $ 272.57万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640864&HistoricalAwards=false
• 关键词: CIF21; DIBBs; EI; Vizier; Streamlined

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).

大数据有望对我们生活的许多方面产生积极影响，但组装数据来回答问题或得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管理或“整理”，在分析数据之前对数据进行组织、细化、清理和合并。 管理通常缓慢且成本高昂，但对于获得有用且值得信赖的答案至关重要。 该项目开发了一款名为 Vizier 的软件工具，旨在简化数据管理，并使不具备计算机科学专业知识的领域专家能够管理自己的数据。 更轻松的管理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，从而使政府、工业和科学领域的多种数据驱动型工作受益。Vizier 具有直观的界面，结合了笔记本电脑和电子表格，使分析师能够快速查看、编辑和修改数据。 此功能得到了与手动管理操作无缝集成的自动化数据清理步骤框架的补充。 Vizier 的核心是一个用于管理策展工作流程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并跟踪数据的沿袭。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 可以实现无遗憾的探索和策展，其中对数据及其传递影响的任何更改都可以撤消。通过学习过去的管理历史，该系统还将能够为用户提供与上下文相关的建议，以进行额外的管理操作。该奖项由高级网络基础设施部门颁发，并得到 NSF 社会、行为和经济科学理事会（美国国家科学基金会社会、行为和经济科学部门）的共同支持。社会和经济科学）。

项目成果

Using Reenactment to Retroactively Capture Provenance for Transactions

使用重演来追溯捕获交易的来源

• DOI: 10.1109/tkde.2017.2769056
• 发表时间: 2017
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Arab, Bahareh Sadat;Gawlick, Dieter;Krishnaswamy, Vasudha;Radhakrishnan, Venkatesh;Glavic, Boris
• 通讯作者: Glavic, Boris

Beta Probabilistic Databases: A Scalable Approach to Belief Updating and Parameter Learning

Beta 概率数据库：一种可扩展的置信更新和参数学习方法

• DOI: 10.1145/3035918.3064026
• 发表时间: 2017
• 期刊: Proceedings of the 2017 ACM International Conference on Management of Data
• 作者: Meneghetti, Niccolo';Kennedy, Oliver;Gatterbauer, Wolfgang
• 通讯作者: Gatterbauer, Wolfgang

Loki: Streamlining Integration and Enrichment

Loki：简化集成和丰富

• 发表时间: 2020
• 期刊: Human in the Loop Data Analytics
• 作者: Spoth, William;Kumari, Poonam;Kennedy, Oliver;Nargesian, Fatemeh
• 通讯作者: Nargesian, Fatemeh

Putting Things into Context: Rich Explanations for Query Answers using Join Graphs

• DOI: 10.1145/3448016.3459246
• 发表时间: 2021-03
• 期刊: Proceedings of the 2021 International Conference on Management of Data
• 作者: Chenjie Li;Zhengjie Miao;Qitian Zeng;Boris Glavic;Sudeepa Roy

Approximate summaries for why and why-not provenance

• DOI: 10.14778/3380750.3380760
• 发表时间: 2020-01
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Seok-Gyun Lee;Bertram Ludäscher;Boris Glavic