CCF: Medium: Enabling Real-Time Quantitative Decision Making over Streaming Data

有效的物联网（IoT）系统的一个关键组成部分是根据接收到的数据实时做出决策的能力。虽然在不同的应用程序中做出决策的确切逻辑需要特定于领域的洞察力，但它通常依赖于以高效和增量的方式计算大数据流的定量摘要。由于大量的数据和对可用内存和响应时间的硬性限制，编程所需的逻辑是具有挑战性的。该项目旨在通过设计一种具有适合处理流数据的自然和高级结构的查询语言来帮助物联网程序员应对这一挑战，并由编译器和运行时系统支持，以方便部署，同时满足所需的准确性，内存占用和实时响应的限制。查询语言的设计借鉴了两个不同范例的见解：用于处理流数据的关系查询语言的扩展，以及用于运行时监视和同步编程的基于状态的语言。语言结构的新颖集成允许程序员赋予输入数据流逻辑层次结构，也可以使用关系结构按键划分输入数据并合并来自不同传感器的数据流。虽然近似理论和流算法允许在编译过程中探索语言特性、准确性和处理时间之间的权衡，但分布式平台（如Apache Storm）用于高性能处理。两个应用领域，设备监测生理病人数据和动态监测网络流量异常，用于实验评估。一个关于可预测的实时决策的课程模块正在开发中，它可以用于数据科学的本科课程、数据库系统和网络物理系统的研究生课程、暑期学校和辅导课。该奖项反映了美国国家科学基金会的法定使命，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。

A key component of an effective Internet of Things (IoT) system is the ability to make decisions in real-time in response to data it receives. While the exact logic for making decisions in different applications requires domain-specific insights, it typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. Programming the desired logic is challenging due to the enormous volume of data and hard constraints on available memory and response time. This project aims to assist IoT programmers in meeting this challenge by designing a query language with natural and high-level constructs suitable for processing streaming data, supported by a compiler and run-time system to facilitate deployment while meeting the constraints of desired accuracy, memory footprint, and real-time response.The design of the query language draws upon insights from two distinct paradigms: extensions of relational query languages for handling streaming data, and state-based languages for runtime monitoring and synchronous programming. The novel integration of linguistic constructs allows the programmer to impart input data stream a logical hierarchical structure and also employ relational constructs to partition the input data by keys and to merge data streams from different sensors. While theory of approximation and streaming algorithms allows exploration of trade-offs among language features, accuracy, and processing time during compilation, distributed platforms such as Apache Storm are used for high performance processing. Two application domains, device monitoring for physiological patient data and dynamic monitoring of network traffic for anomalies, are used for experimental evaluation. A course module on predictable real-time decision making is being developed, and can be used in an undergraduate course on data science, in graduate courses on database systems and cyber-physical systems, in summer schools, and as tutorials.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.