大数据分析架构设计
Big data analytics architecture design
摘要：
Objective.我们提出了一种方法来推理目标，障碍，并选择合适的大数据解决方案架构，满足质量目标的偏好和利益相关者在决策结果的不确定性的存在下的约束。该方法将突出可能阻碍实现目标的情况。将对它们进行评估和解决，以生成架构解决方案的完整需求。法该方法采用面向目标的建模，以确定质量目标失败的障碍和相应的解决策略。它结合了模糊逻辑来探索解决方案架构中的不确定性，并为制造系统的大数据启用过程找到一组最佳的架构决策。结果.该方法为制造系统中采用大数据分析平台的最新技术带来了两项创新。首先，一个系统的面向目标的建模，用于探索在需求层面将制造系统与数据分析平台集成的目标和障碍，其次，在不确定性下结合利益相关者的偏好对架构决策进行系统分析。该方法的有效性说明了一个超连接的制造协作系统，以一个新的大数据架构的重新设计方案。关键词大数据，大数据分析平台，制造系统，目标导向建模，模糊逻辑
英文摘要：
Objective. We propose an approach to reason about goals, obstacles, and to select suitable big data solution architecture that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method. The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result. The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems. Firstly, A systematic goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and, secondly, A systematic analysis of the architectural decisions under uncertainty incorporating the preferences of stakeholders. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to a new big data architecture. Keywords. big data, big data analytics platforms, manufacturing systems, goal-oriented modeling, fuzzy logic
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
