Requirements Engineering for the Cognitive Enterprise

认知企业的需求工程

• 批准号: RGPIN-2019-06592
• 负责人: Yu, Eric
• 金额: $ 2.04万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682740
• 关键词: Requirements; Engineering; Cognitive; Enterprise

In information systems engineering, conceptual models (high-level abstractions) play an important role in enabling business architects and stakeholders to conceive of innovative solutions to business problems, serving to define requirements for system development and implementation. *** Today, a new generation of IT systems is presenting fresh challenges for requirements engineering (RE). These systems leverage the massive amounts of data from social media, sensor networks, mobile devices, and all things digital. Unlike traditional systems whose power derives from efficient and accurate execution of transactions and processes, these new software systems help generate insights and predictions. They have new capabilities and properties that are not well represented or analyzed through existing requirements models. Machine learning, artificial intelligence, and associated data science and technologies have created a modern gold rush for IT, yet organizations struggle to conceptualize their effective application in actual settings. This gap between business potential and technical possibilities suggests that new abstractions are called for at the requirements level. *** The term “cognitive computing” has been used to suggest that a new generation of IT is approaching human cognitive abilities, even surpassing humans in some tasks. From a requirements engineering standpoint, complex organizations are in fact already cognitive enterprises, regardless of the extent to which cognitive technologies are deployed. Organizations sense the environment and interpret the data, extract patterns, make predictions, formulate theories and strategies, take actions and monitor their effects, feeding back to the next iteration of the cognitive cycle. *** This proposal outlines a research agenda to develop a Requirements Engineering framework for enterprise computing based on a notion of cognitive architecture of the enterprise. Enterprise initiatives to deploy data-driven technologies such as machine learning and AI will be conceived and tested using technology-independent conceptual models, in terms of strengthening the cognitive capabilities of the enterprise to accomplish organizational objectives. The cognitive architecture of the enterprise in its current (as-is) state would be analyzed for deficiencies and baseline capabilities. The framework, which includes a visual language notation, will support and guide enterprise strategists and architects to map out and explore desirable (to-be) cognitive architectures according to the organization's maturity level. The abstract architecture will be implemented via a network of cooperating human and technological capabilities.*** Research results will include a modeling language, analysis and design methods, supporting tools, a usage methodology, as well as sample design patterns. The methodology will include guidance on mapping abstract cognitive architectures to technology implementations. **

在信息系统工程中，概念模型(高级抽象)在使业务架构师和利益相关者能够构思针对业务问题的创新解决方案方面发挥重要作用，用于定义系统开发和实现的需求。*今天，新一代IT系统对需求工程(RE)提出了新的挑战。这些系统利用来自社交媒体、传感器网络、移动设备和所有数字事物的海量数据。与传统系统不同，传统系统的能力来自高效和准确地执行交易和流程，这些新的软件系统有助于产生洞察力和预测。它们具有通过现有需求模型无法很好地表示或分析的新功能和属性。机器学习、人工智能以及相关的数据科学和技术为IT创造了一股现代淘金热，但组织仍在努力将它们在实际环境中的有效应用概念化。业务潜力和技术可能性之间的这种差距表明，需要在需求级别进行新的抽象。*“认知计算”一词被用来暗示新一代信息技术正在接近人类的认知能力，甚至在某些任务上超过人类。从需求工程的角度来看，复杂的组织实际上已经是认知企业，无论认知技术被部署到什么程度。组织感知环境并解释数据，提取模式，做出预测，制定理论和策略，采取行动并监测其效果，反馈到认知周期的下一次迭代。*本提案概述了基于企业认知体系结构的概念开发企业计算需求工程框架的研究议程。部署机器学习和人工智能等数据驱动技术的企业倡议将使用独立于技术的概念模型进行构思和测试，以加强企业实现组织目标的认知能力。将分析企业在其当前(原样)状态下的认知体系结构的不足之处和基线能力。该框架包括可视化语言符号，将支持和指导企业策略师和架构师根据组织的成熟度级别规划和探索理想的(未来)认知体系结构。抽象架构将通过协作的人力和技术能力网络实施。*研究成果将包括建模语言、分析和设计方法、支持工具、使用方法以及样本设计模式。该方法将包括将抽象认知架构映射到技术实现的指导。**