Several architecture frameworks for software, systems, and enterprises have
been proposed in the literature. They identified various stakeholders and
defined architecture viewpoints and views to frame and address stakeholder
concerns. However, the stakeholders with data science and Machine Learning (ML)
related concerns, such as data scientists and data engineers, are yet to be
included in existing architecture frameworks. Therefore, they failed to address
the architecture viewpoints and views responsive to the concerns of the data
science community. In this paper, we address this gap by establishing the
architecture frameworks adapted to meet the requirements of modern applications
and organizations where ML artifacts are both prevalent and crucial. In
particular, we focus on ML-enabled Cyber-Physical Systems (CPSs) and propose
two sets of merit criteria for their efficient development and performance
assessment, namely the criteria for evaluating and benchmarking ML-enabled
CPSs, and the criteria for evaluation and benchmarking of the tools intended to
support users through the modeling and development pipeline. In this study, we
deploy multiple empirical and qualitative research methods based on literature
review and survey instruments including expert interviews and an online
questionnaire. We collect, analyze, and integrate the opinions of 77 experts
from more than 25 organizations in over 10 countries to devise and validate the
proposed framework.

本研究通过建立适应现代应用和组织要求的架构框架，针对数据科学和机器学习相关的利益相关者的需求，提出了用于评估和基准测试 ML-enabled CPS 的标准以及支持用户进行建模和开发管道的工具评估的标准。通过文献回顾、专家访谈和在线问卷调查等多种经验和定性研究方法，我们收集、分析和整合了来自 25 多个国家的 10 多个组织中 77 位专家的意见，以制定和验证所提出的框架。