PFI-TT: A Hybrid Scalable Data Management System Providing Deep Access to the Scientific Knowledge in Data Science

PFI-TT：混合可扩展数据管理系统，提供对数据科学中科学知识的深入访问

• 批准号: 2345794
• 负责人: Michael Gubanov
• 金额: $ 55万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345794&HistoricalAwards=false
• 关键词: PFI; TT; Hybrid; Scalable; Data

The broader impact of this project lies in its transformative potential for accessing scientific knowledge in the field of data science. The data management system, algorithms, and Knowledge Graphs (KGs) will improve the way new advances in data science are searched. By intelligently reformulating the search questions, users will experience a direct path to the most relevant results, significantly reducing the time spent on the usual trial-and-error searches, enhancing the productivity of data scientists and other consumers of scientific knowledge. The potential societal impact will be substantial, as the advantages introduced by the algorithms and systems could lead to broad and significant cost savings in research expenditures for government, companies, and universities. This approach challenges the current de-facto standard of keyword-search and Large Language Models (LLMs). The LLMs are sophisticated artificial intelligence models designed to understand and generate human-like text based on the input they receive. This research promises a more effective and resource-efficient method for accessing scientific knowledge. The project will provide valuable experiential learning for students, fostering skills in technology and entrepreneurship and empowering them to translate their research into successful, student-led, high-tech startups.The project envisions a novel solution to access knowledge in the scientific literature in the form of an intelligent and scalable hybrid data management system. The solution aims to provide users with a user-friendly interface for interacting with scientific concepts, accurately extracted and organized from a myriad of peer-reviewed scientific publications organized in a KG. This KG will surpass traditional approaches, maintained by experts by scale, depth, and freshness of knowledge, continuously learning from the latest peer-reviewed literature. The system's value proposition lies in its potential to achieve time savings of several orders of magnitude compared to current scientific search methods, while also delivering more comprehensive search results. The approach focuses on unsupervised extraction of the main search concepts and their structure from the state-of-the-art publications, organizing them into a hierarchy. This structured approach will simplify access to complex knowledge, enhancing the overall quality of search results and minimizing time spent on the search. The versatility of the approach allows for generalization to other domains, promising broad applicability and impact.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.

该项目更广泛的影响在于它在获取数据科学领域的科学知识方面的变革潜力。数据管理系统、算法和知识图(KG)将改进搜索数据科学新进展的方式。通过智能地重新制定搜索问题，用户将体验到一条通往最相关结果的直接途径，大大减少了花费在通常反复试验搜索上的时间，提高了数据科学家和其他科学知识消费者的生产率。潜在的社会影响将是巨大的，因为算法和系统引入的优势可以为政府、公司和大学节省广泛而显著的研究支出。这种方法挑战了当前关键字搜索和大型语言模型(LLM)的事实标准。LLMS是一种复杂的人工智能模型，旨在理解并根据它们接收的输入生成类似人类的文本。这项研究承诺为获取科学知识提供一种更有效、更节约资源的方法。该项目将为学生提供有价值的体验式学习，培养技术和创业技能，并使他们能够将他们的研究转化为成功的、学生主导的高科技初创企业。该项目设想了一种新的解决方案，以智能和可扩展的混合数据管理系统的形式获取科学文献中的知识。该解决方案旨在为用户提供一个用户友好的界面，以便与科学概念互动，这些概念准确地摘录和组织在KG中的无数同行评议的科学出版物中。这种KG将超越由专家在规模、深度和知识新鲜度方面保持的传统方法，不断从最新的同行评议文献中学习。该系统的价值主张在于，与目前的科学搜索方法相比，它有可能节省几个数量级的时间，同时还可以提供更全面的搜索结果。该方法侧重于从最先进的出版物中无监督地提取主要搜索概念及其结构，并将它们组织成一个层次结构。这种结构化的方法将简化对复杂知识的获取，提高搜索结果的整体质量，并最大限度地减少搜索所花费的时间。该方法的多功能性允许推广到其他领域，承诺广泛的适用性和影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。