Software Engineering for IoT Data-Driven Machine Learning Applications

许多组织越来越多地采用人工智能（AI），特别是机器学习（ML），它有可能为行业和社会带来巨大价值。然而，我们在最终的软件系统中经历了失败和缺陷。主要原因是人工智能和机器学习产生的开发范式的转变。机器学习任务通常与机器学习算法和技术的开发和增强有关，而软件工程（SE）主要关注软件需求规范、测试、部署和发展。机器学习应用程序与传统软件的不同之处在于，它们的逻辑不是明确编程的，而是通过不断从数据中学习而自动创建的。基于ml的系统的开发过程涉及不同的活动，包括数据收集、训练和模型评估。这些任务主要由机器学习和领域专家执行，在较小程度上由软件工程师执行。然而，由于ML系统的开发方法不同，整个系统的开发需要针对已经建立的SE流程的新方法，或者可能是全新的方法。除此之外，我们目前正在见证涉及高速和大容量数据流的新一代软件应用，例如与物联网（IoT）相关的软件应用。物联网是指将任何设备连接到互联网，实现设备、车辆和其他现实世界元素的数字化和基于服务的协调。因此，毫不奇怪，物联网已被公认为以多种形式影响社会和工业的重大范式转变，从远程医疗到智能交通、智能电网和工业4.0。所有这些应用在被ML模型用于训练和推理之前，都需要高效地获取、处理和管理高速和大容量的数据。该研究计划旨在在建模、设计和部署物联网数据驱动的机器学习应用方面取得实质性进展。在基础研究层面，重点将放在为设计、发展和部署实用的基于ml的系统建立新颖、可靠的SE方法上。研究结果将使软件工程师能够将机器学习训练的模型转换为工业强度的生产质量的机器学习应用程序。这项研究将通过提供一种结构化的方法来开发处理物联网数据的机器学习应用程序，从而使智能工厂、智能建筑、先进制造业等多种加拿大行业在自动化过程中受益。拟议的研究计划还将提供巨大的HQP培训机会，并为加拿大行业提供使用物联网数据的工程ML应用专家，这一职业在未来五到十年内将继续保持高需求。

Many organizations have increasingly adopted artificial intelligence (AI), particularly machine learning (ML), with the potential to deliver enormous value to industry and society. However, we experience failures and shortcomings in the resulting software systems. The main reason is the shift in the development paradigm generated by AI and ML. While ML tasks are typically related to the development and enhancement of ML algorithms and techniques, software engineering (SE) primarily focuses on the software requirements specification, testing, deployment, and evolution. ML applications differ from traditional software in that their logic is not explicitly programmed but instead automatically created by continuously learning from data. The ML-based systems' development process involves different activities, including data collection, training, and model evaluation. These tasks are mostly performed by ML and domain experts and, to a lesser extent, by software engineers. Nevertheless, due to the different approaches in the development of ML systems, the entire system's development requires new methods for already established SE processes or possibly wholly new approaches. In addition to the above, we are currently witnessing a new generation of software applications which involve high speed and high volume data streams, such as software applications related to the Internet of Things (IoT). IoT is about connecting any device to the Internet, enabling the digitization and service-based coordination of devices, vehicles, and other real-world elements. Therefore, it is no surprise that IoT has been recognized as a significant paradigm shift impacting both society and industry in numerous forms ranging from telemedicine to smart transportation, smart grids, and Industry 4.0. All these applications require efficient acquisition, processing, and management of high speed and high volume data before being used for training and reasoning by ML models. This research program aims to bring substantial advancements in modeling, designing, and deploying IoT data-driven machine learning applications. At the fundamental research level, the focus will be on establishing novel, and sound SE approaches for designing, evolving, and deploying practical ML-based systems. The research results will enable software engineers to transition ML-trained models to industry-strength production-quality ML applications. This research will benefit a diverse Canadian industry, such as smart factories, smart buildings, advanced manufacturing, in their automation processes by providing a structured approach to develop ML applications dealing with IoT data. The proposed research program will also stage a tremendous HQP training opportunity and equip Canadian industries with experts in engineering ML applications using IoT data, a profession believed to continue to be in high demand in the next five to ten years.