III: Small: Collaborative Research: An Optimization Framework for Designing Derived Attributes with Humans-in-the-loop

属性设计是大数据科学管道中最具挑战性的方面之一，其中原始属性需要转换为易于解释的属性，这些属性可以帮助数据科学家进行临时数据探索和构建预测模型。不幸的是，当前用于属性设计的自动化技术不能为最终用户提供足够的可解释性，而且由人类数据科学家设计的属性非常缓慢，并且严重依赖于领域专业知识。该项目将开发一种新颖的变革性方法，使一群业余人类工作者能够参与属性设计的计算循环。它将使需要有效应用大数据科学的各个领域受益。此外，该项目将通过吸引业余工作者和促进数据科学劳动力的系统发展来改善该国的经济福祉。在教育方面，该项目将有重要的教育和推广活动，涵盖K-12以及研究生数据科学教育。该研究涉及开发一套算法和技术，以理解将人类工作人员集成到属性设计中的机遇和挑战，其灵感来自机器学习中的集成方法。主要的重点是迭代方法，以指导业余的人类工作者，即使是有限的领域专业知识，也可以为数据探索和预测建模提出新的属性。该研究通过将理论证明的属性设计算法与现实世界数据科学任务的特定应用细节相结合，为工程技术取得了根本性的进步。原型将被严格评估，涉及来自多个应用领域和人类工作人员的数据集。这项研究的结果将刺激下一代人在循环计算的重要研究，以及对巨大，高维和深不可测的数据集的影响数据探索。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Attribute design is one of the most challenging aspects of the Big Data Science pipeline, where raw attributes need to be transformed into easily-interpretable attributes that can aid data scientists in ad-hoc data exploration and building predictive models. Unfortunately, current automated techniques for attribute design do not offer adequate interpretability to the end user, and attribute designed by human data scientists is painstakingly slow and heavily reliant on domain expertise. This project will develop a novel and transformative approach to enable an ensemble of amateur human workers to be involved in the computational loop for attribute design. It will benefit various domains that require effective applications of Big Data Science. In addition, the project will improve the economic well-being of the country by involving amateur workers and fostering systematic development of a data science workforce. On the educational front, the project will have significant education and outreach activities that span K-12 as well as graduate Data Science education. The research involves developing a suite of algorithms and techniques for understanding the opportunities and challenges of involving an ensemble of human workers in attribute design, which is inspired by ensemble methods in Machine Learning. The main focus is on iterative methods to guide amateur human workers even with limited domain expertise to suggest new attributes for data exploration and predictive modeling. The research makes fundamental advancements to engineering by integrating theoretically-proven attribute design algorithms with application-specific details of real-world data science tasks. A prototype will be rigorously evaluated involving datasets from several application domains and human workers. The outcomes of this research will spur significant research in next generation human-in-the-loop computing, as well as impact data exploration over huge, high dimensional and unfathomable datasets.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.