SHF: Medium: Collaborative Research: Computer-Aided Programming for Data Science

SHF：媒介：协作研究：数据科学计算机辅助编程

基本信息

批准号：
1762299
负责人：
Isil Dillig
金额：
$ 105万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1762299&HistoricalAwards=false
关键词：
SHF Medium Collaborative Research Computer

项目摘要

The goal of this project, named DataWizard, is to dramatically simplify the effort that is currently required for data analytics through the use of computer-aided programming. Specifically, this project aims to semi-automate data collection, querying, and wrangling tasks by automatically generating programs from informal specifications. As a result, the DataWizard project will allow domain scientists to focus on more interesting data analytics and visualization tasks, leaving the "grunt work" of data science to computer-aided programming tools. The project will also advance the state-of-the-art in automated program synthesis and natural language processing and apply these techniques to the burgeoning field of big data analytics. From a technical perspective, the goals of the DataWizard project are three-fold. First, this project develops novel programming-by-example and information extraction techniques to address challenges that arise in data collection, including consolidation of different data sources, transformations between hierarchical and relational data, and extraction of information from unstructured data sources. Second, this project explores new techniques for querying data using natural language descriptions. In particular, this project considers data extraction from relational and noSQL databases as well as semi-structured data sources, such as XML and JSON. Third, this project develops novel program synthesis methods for automating data wrangling, cleaning, and imputation tasks that commonly arise in data analytics. Overall, these techniques make it significantly easier for data scientists to gain insights from messy data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的目的名为DataWizard，是通过使用计算机辅助编程来大大简化数据分析所需的精力。具体而言，该项目旨在通过自动从非正式规范生成程序来收集半自动群数据，查询和纠纷任务。结果，DataWizard项目将允许域科学家专注于更有趣的数据分析和可视化任务，将数据科学的“咕unt工作”留给计算机辅助编程工具。该项目还将推进自动化程序合成和自然语言处理的最新技术，并将这些技术应用于大数据分析的新兴领域。从技术的角度来看，数据瓦式项目的目标是三倍。首先，该项目开发了典型编程和信息提取技术，以解决数据收集中出现的挑战，包括整合不同的数据源，层次结构和关系数据之间的转换以及从非结构数据源中提取信息。其次，该项目探讨了使用自然语言描述查询数据的新技术。特别是，该项目考虑了从关系和NOSQL数据库以及半结构化数据源（例如XML和JSON）提取的数据。第三，该项目开发了用于自动化数据分析中通常出现的数据争吵，清洁和插入任务的新型程序合成方法。总体而言，这些技术使数据科学家从杂乱无章的数据中获取见解变得更加容易。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准来通过评估来获得支持的。