GOALI: Computer Simulation Analytics

大数据分析用于探索海量交易数据，如客户购买历史、医疗记录或手机使用情况，以发现有助于预测和理解的预期和意外关系。工程师和管理科学家使用动态随机模拟来设计和改进易受风险影响的工业、服务和金融系统。模拟还会生成大量的合成事务数据，但当前的做法是首先将这些数据提取到高级别的汇总性能度量中，然后将其丢弃，这使得评估风险或预测实际系统行为变得困难。数据分析在商业和工业中的成功将导致模拟用户期望从他们的模拟中获得同样的细粒度分析，如果他们无法获得，他们可能会得出结论，模拟是无关紧要的。这个GOALI学术联络机会(GOALI)研究项目将通过将模拟视为对尚不存在的系统的数据分析，为动态随机模拟的数据分析处理提供基础和概念验证的第一步。其结果将是商业和工业的更好和更强大的系统设计和控制决策。与工业合作伙伴SAS研究所的合作将确保研究具有广泛的相关性并将结果付诸实施。计划了三个核心主题：作为系统控制的先导的模拟分析，用于全面比较系统设计的模拟分析，以及通过动态元模型的模拟分析。重点是通过保留模拟样本路径来促进后模拟分析，模拟样本路径可能是由旨在实现特定狭隘目标(如系统优化)的模拟实验生成的。将创建新的可视化和统计分析方法，使模拟用户能够解决数据科学家通常解决的问题，但在模拟环境中，数据是动态的、依赖时间的样本路径，而不是客户实例。这将需要针对监督和非监督学习的新的统计方法。还将讨论外地数据分析中通常不会出现的问题，如比较暴露在同一模拟不确定性来源下的替代系统设计。

Big data analytics are used to explore vast quantities of transactional data, such as customer purchase histories, medical records, or cell phone usage, to discover anticipated and unanticipated relationships that are useful for prediction and understanding. Dynamic stochastic simulations are used by engineers and management scientists to design and improve industrial, service and financial systems that are subject to risk. Simulations also generate large quantities of synthetic transactional data, but current practice is to first distill these data into high-level summary performance measures, and then to discard it, making it difficult to evaluate risk or predict actual system behavior. The success of data analytics in business and industry will lead simulation users to expect the same sort of fine-grained analysis from their simulations, and if they cannot obtain it they may conclude that simulation is irrelevant. This Grant Opportunity for Academic Liaison with Industry (GOALI) research project will provide a foundation and proof-of-concept first steps toward a data analytics treatment of dynamic, stochastic simulation, by considering simulation as data analytics for systems that do not yet exist. The result will be better and more robust system design and control decisions for business and industry. Collaboration with industrial partner SAS Institute will insure that the research is broadly relevant and the results are implemented.Three core topics are planned: simulation analytics as a precursor to system control, simulation analytics for comprehensive comparisons of system designs, and simulation analytics via dynamic metamodels. The focus is on a post-simulation analysis that is facilitated by retaining the simulated sample paths, sample paths that may have been generated by a simulation experiment designed to achieve a specific narrow objective such as system optimization. New visualization and statistical analysis methods will be created to allow simulation users to solve the kinds of problems that data scientists routinely address, but in the simulation context where the data are dynamic, time-dependent sample paths, rather than customer instances. This will require new statistical methods for both supervised and unsupervised learning. Problems that do not typically arise in field data analytics, like the comparison of alternative system designs that are exposed to the same source of simulated uncertainty, will also be addressed.