SHF: Small: Programming Foundations for Real-Time Data Analysis

现代信息处理系统处理大量实时产生并需要立即分析的数据。这些实时数据分析用于提取洞察力，并支持各种应用领域的决策，例如商业智能和患者监控。用于处理实时(流)数据的现有系统侧重于在分布式计算基础设施上提供高效且可伸缩的执行。这些系统的一个常见问题是，它们的行为没有明确规定，这使得很难对它们的正确性进行推理。在许多情况下，流数据分析的分布式执行会导致不可重现的不可预测行为。该项目的创新之处在于开发了一个规范实时数据处理系统行为的正式框架，并使用该框架来确保计算的可重复性。该项目的影响是在编程可信和可重复的流数据分析的实践方面的进步。该项目开发了一个新的编程基础，包括数据流处理的语义、查询语言设计的相关原则和健壮的分布式实现。使用类型规则描述外延语义，该规则捕获数据模型和数据不变量的各种概念。确定了流处理的基本编程抽象，从而实现了复杂流分析的模块化规范。开发了一种保证高效和可重现的分布式执行的实现。该框架的有用性是通过来自企业商业智能和医疗患者监测领域的案例研究来建立的。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。

Modern information-processing systems handle large volumes of data that are produced in real time and need immediate analysis. These real-time data analyses are used to extract insights and enable decision-making in various application domains, such as business intelligence and patient monitoring. Existing systems for processing real-time (streaming) data focus on providing efficient and scalable execution over a distributed computing infrastructure. A common problem with these systems is that their behavior is not clearly specified, which makes it difficult to reason about their correctness. In many cases, the distributed execution of a streaming data analysis gives rise to unpredictable behavior that is not reproducible. The project's novelties are the development of a formal framework for specifying the behavior of real-time data-processing systems and the use of this framework for ensuring the reproducibility of computations. The project's impacts are advances in the practice of programming trustworthy and repeatable streaming data analyses.The project develops a novel programming foundation that encompasses the semantics of data-stream processing, the associated principles of query-language design, and a robust distributed implementation. A denotational semantics is described using a type discipline that captures various notions of data models and data invariants. Basic programming abstractions for stream processing are identified, which enable the modular specification of complex streaming analyses. An implementation is developed that guarantees efficient and reproducible distributed execution. The usefulness of the framework is established with case studies from the domains of enterprise business intelligence and patient monitoring in healthcare.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.