2023/02/09 by Christian Cabrera, Andrei Paleyes, Cabrera, Christian +5 · 1 voice
Business, Management and Accounting · Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #Cloud Computing and Resource Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Engineering (cs.SE) #cs.AI #cs.LG #cs.SE
paper · pdf · doi:10.48550/arxiv.2302.04810
openalex publication_date 2023/02/09 · arxiv published 2023/02/09 · openalex created_date 2023/02/13 · arxiv updated 2025/07/16 · openalex updated_date 2026/07/28
Engineers are deploying ML models as parts of real-world systems with the upsurge of AI technologies. Real-world environments challenge the deployment of such systems because these environments produce large amounts of heterogeneous data, and users require increasingly efficient responses. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging style that equips systems better for integrating ML models. Even though papers on deployed ML systems do not mention DOA, their authors made design decisions that implicitly follow DOA. Implicit decisions create a knowledge gap, limiting the practitioners' ability to implement ML-based systems. \hlbThis paper surveys why, how, and to what extent practitioners have adopted DOA to implement and deploy ML-based systems. We overcome the knowledge gap by answering these questions and explicitly showing the design decisions and practices behind these systems. The survey follows a well-known systematic and semi-automated methodology for reviewing papers in software engineering. The majority of reviewed works partially adopt DOA. Such an adoption enables systems to address requirements such as Big Data management, low latency processing, resource management, security and privacy. Based on these findings, we formulate practical advice to facilitate the deployment of ML-based systems.