SBIR Phase II: Scalable Collaborative Analytical Modeling

SBIR 第二阶段：可扩展的协作分析建模

• 批准号: 1831280
• 负责人: Stephen Brady
• 金额: $ 73.09万
• 依托单位: Fact Labs Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831280&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Scalable; Collaborative

The broader impact/commercial potential of this Small Business Innovation Research Phase II project is to enable organizations - whether businesses, governments, or non-profits - to make more informed, more data-driven decisions. All organizations must decide how to allocate limited resources and do so in the context of meeting a set of objectives, such as profit, social wellbeing, or health. Modeling as a process and models as artifacts of that process allow decision makers to understand data through the lens of objectives and to then make decisions; data alone, no matter how much, cannot make decisions. As more aspects of the world are instrumented and captured digitally, the breadth and quantity of data will out of necessity require larger, more complex models to be built. Organizations will need a modeling workflow and supporting tools that scale with these demands.This Small Business Innovation Research (SBIR) Phase II project addresses the challenge of many users collaboratively building and maintaining analytical models that are consistent and reproducible while allowing for divergent and convergent change. This project will address the resource management challenges identified in development and the interactivity gaps identified in user testing of the Phase I prototype. The result will be a commercial software application for building models that manages code, data, and the evolution of both over time by many users.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.

这个小企业创新研究第二阶段项目的更广泛的影响/商业潜力是使组织-无论是企业，政府还是非营利组织-做出更明智，更数据驱动的决策。所有组织都必须决定如何分配有限的资源，并在实现一系列目标的背景下这样做，如利润，社会福利或健康。作为过程的建模和作为该过程的工件的模型允许决策者通过目标的透镜来理解数据，然后做出决策;单独的数据，无论有多少，都不能做出决策。随着世界上越来越多的方面被数字化和捕获，数据的广度和数量将不可避免地需要建立更大、更复杂的模型。组织将需要一个建模工作流程和支持工具来满足这些需求。这个小型企业创新研究（SBIR）第二阶段项目解决了许多用户协作构建和维护分析模型的挑战，这些模型是一致的和可复制的，同时允许发散和收敛的变化。该项目将解决在开发过程中发现的资源管理挑战和在第一阶段原型用户测试中发现的互动差距。该奖项反映了NSF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。