FMitF: Track II: Programming by Demonstration for the Browser with Applications in Data Science

FMITF：轨道 II：通过数据科学应用程序对浏览器进行演示编程

• 批准号: 1918027
• 负责人: Rastislav Bodik
• 金额: $ 9.89万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918027&HistoricalAwards=false
• 关键词: FMitF; Track; II; Programming; Demonstration

Future work will revolve around consuming data, deriving insights, and making data-informed decisions. While data analysis and machine learning can be significantly automated, an irreducibly human part of the data-driven future is data collection and curation. Currently, data collection is done either by experienced engineers or through manual data collection. Programming by Demonstration (PBD) has recently allowed non-experts to author automatic web-scraping programs, which collect data efficiently and reliably. This project will extend a previously developed system for browser-based PBD and evaluate it in a large-scale deployment. The scale will be large both in terms of users and the size of data that will be collected. The system development part of the project will focus on two parts: (1) Implementing and deploying algorithms for large-scale deployment of the Helena browser-based PBD system. This work will include parallelization of synthesized web-scraper programs, making programs robust to failures of web sites and the network, and adapting the parallel execution to optimize for either the data collection rate or the cost of datacenter time. (2) Developing a next-generation user interface that will include support for debugging the synthesized programs. The target audience of these tools will be teams of data-driven social scientists with whom the PI team has had long-term collaborations. These teams have been using the Helena PBD system for collecting datasets that improve strategies of non-profits and influence government policies. The evaluation will investigate both the system performance and the usability of the novel and critical parts of the Helena ecosystem.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.

未来的工作将围绕消费数据、获得见解和做出基于数据的决策。虽然数据分析和机器学习可以显著自动化，但数据驱动的未来中不可减少的人类部分是数据收集和管理。目前，数据收集由经验丰富的工程师或通过手动数据收集完成。 演示编程（PBD）最近允许非专家编写自动Web抓取程序，这些程序可以有效可靠地收集数据。 该项目将扩展先前开发的基于浏览器的PBD系统，并在大规模部署中对其进行评估。 从用户和收集的数据量来看，规模都将很大。 本项目的系统开发部分将集中在两个部分：（1）实现和部署基于Helena浏览器的PBD系统的大规模部署算法。这项工作将包括合成的web scraper程序的并行化，使程序对网站和网络的故障具有鲁棒性，并调整并行执行以优化数据收集速率或数据中心时间成本。(2)开发下一代用户界面，包括对调试合成程序的支持。 这些工具的目标受众将是PI团队长期合作的数据驱动的社会科学家团队。 这些团队一直在使用海伦娜PBD系统收集数据集，以改善非营利组织的战略并影响政府政策。 评估将调查系统性能和海伦娜生态系统的新颖和关键部分的可用性。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。