Requirements Modeling for Enterprise Applications of Machine Learning and Artificial Intelligence

在信息系统工程中，需求模型在使业务架构师和涉众能够构想针对业务问题的创新解决方案方面发挥着重要作用，用于定义系统开发和实现的需求。今天，新一代的IT系统为需求工程提出了新的挑战。这些系统利用来自社交媒体、传感器网络、移动设备以及交易数据的大量数据。与传统系统不同，传统系统的功能来自于高效准确地执行规定的流程和交易，这些新的软件系统有助于生成见解和预测，并使用机器学习（ML）和人工智能（AI）提出建议。它们具有新的功能和属性，这些功能和属性没有通过现有的需求模型很好地表示或分析。越来越多地结合ML/AI的应用示例包括销售预测、个性化营销、医疗保健诊断和推荐以及导航指南。

In information systems engineering, requirements models 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. These systems leverage the massive amounts of data from social media, sensor networks, mobile devices, as well as transactional data. Unlike traditional systems whose power derives from efficient and accurate execution of prescribed processes and transactions, these new software systems help generate insights and predictions and make recommendations using machine learning (ML) and artificial intelligence (AI). They have new capabilities and properties that are not well represented or analyzed through existing requirements models. Some examples of applications increasingly incorporating ML/AI include sales prediction, personalized marketing, diagnosis and recommendation in healthcare, and navigation guides. The objective of the proposed research is to develop conceptual modeling techniques to support requirements engineering for enterprise applications of ML and AI. The modeling techniques will enable analysts to systemically explore and analyze alternative ways of positioning ML/AI functionalities within an organizational context (i.e., within a business process and the larger business and IT environment) so as to meet organizational and business goals. The research will build upon and extend existing requirements modeling approaches, including process modeling, goal modeling, and agent modeling, and will explore cognitive modeling, to address the special needs of enterprise ML/AI. Research results will including novel modeling language features, notations, methods and techniques. A combination of analytical and empirical research methods will be used. Research results will be evaluated through industrial case studies.