With the upsurge of interest in artificial intelligence machine learning (ML) algorithms, originally developed in academic environments, are now being deployed as parts of real-life systems that deal with large amounts of heterogeneous, dynamic, and high-dimensional data. Deployment of ML methods in real life is prone to challenges across the whole system life-cycle from data management to systems deployment, monitoring, and maintenance. Data-Oriented Architecture (DOA) is an emerging software engineering paradigm that has the potential to mitigate these challenges by proposing a set of principles to create data-driven, loosely coupled, decentralised, and open systems. However DOA as a concept is not widespread yet, and there is no common understanding of how it can be realised in practice. This review addresses that problem by contextualising the principles that underpin the DOA paradigm through the ML system challenges. We explore the extent to which current architectures of ML-based real-world systems have implemented the DOA principles. We also formulate open research challenges and directions for further development of the DOA paradigm.
翻译:随着对最初在学术环境中开发的人工智能机器学习算法的兴趣剧增,现在正在将人工智能机器学习算法作为实际生活系统的一部分来部署,这些算法涉及大量多样化、动态和高维数据。在现实生活中部署ML方法容易在整个系统生命周期中遇到挑战,从数据管理到系统部署、监测和维护。数据导向架构是一个新出现的软件工程范式,有可能通过提出一套原则来减轻这些挑战,从而创建数据驱动、松散、分散和开放的系统。然而,DOA作为一个概念尚未普及,对如何在实践中实现这个概念没有共同的理解。本审查通过ML系统的挑战,将DOA范式所根据的原则背景化,从而解决这一问题。我们探讨了以ML为基础的现实世界系统目前的结构在多大程度上实施了DOA原则。我们还为进一步发展DA范式制定了开放的研究挑战和方向。