SBIR Phase II: Scalable Collaborative Analytical Modeling

小型企业创新研究第二阶段项目的更广泛影响/商业潜力是使组织--无论是企业、政府还是非营利组织--能够做出更明智、更受数据驱动的决策。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情况下这样做，例如利润、社会福利或健康。将建模作为一个过程，并将模型作为该过程的产物，使决策者能够通过目标的镜头理解数据，然后做出决策；仅靠数据，无论有多少，都不能做出决策。随着世界上更多的方面被数字化仪器和捕获，数据的广度和数量必然需要建立更大、更复杂的模型。组织将需要随这些需求扩展的建模工作流和支持工具。此小型企业创新研究(SBIR)第二阶段项目解决了许多用户协作构建和维护一致且可重现的分析模型，同时允许不同和收敛的更改的挑战。该项目将解决在开发中查明的资源管理挑战和在第一阶段原型的用户测试中查明的互动差距。其结果将是一个商业软件应用程序，用于构建管理代码、数据以及许多用户随着时间推移管理两者的演变的模型。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。

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.