[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118663-en":3,"doc-seo-118663-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118663,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Software architecture for machine learning to aid sustainable digital transformation - A systematic mapping study","Rapid adoption of machine learning-based software solutions offers new pathways to address societal challenges, while ongoing digital transformation embeds these solutions across application domains. The article investigates how software architecture for machine learning applications can support sustainable digital transformation and improve software system sustainability over time. It conducts a systematic mapping study of the literature at the intersection of sustainable digital transformation, machine learning, and software architecture, finding a small intersection, uneven sustainability coverage, diverse application domains, and low maturity of ML adoption.","Information and Software Technology 190 (2026) 107931  \n| Software architecture for machine learning to aid sustainable digital\u003Cbr>transformation: A systematic mapping study✩\u003Cbr>Lech Bialek a,b ,∗, Rix Groenboom b, Vasilios Andrikopoulos aa University of Groningen, Nijenborgh 9, 9747AG, Groningen, The Netherlands\u003Cbr>b Hanze Univeristy of Applied Sciences, Zernikeplein 11, 9747AS, Groningen, The Netherlands |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Digital transformation Sustainability Machine learning Software architecture MLOps |  | Context: Rapid developments and adoption of machine learning-based software solutions have enabled novel ways to tackle our societal problems. The ongoing digital transformation has led to the incorporation of these software solutions in just about every application domain. Software architecture for machine learning applications used during sustainable digital transformation can potentially aid the evolution of the underlying software system adding to its sustainability over time.\u003Cbr>Objective: Software architecture for machine learning applications in general is an open research area. When applying it to sustainable digital transformation it is not clear which of its considerations actually apply in this context. We therefore aim to understand how the topics of sustainable digital transformation, software architecture, and machine learning interact with each other.\u003Cbr>Methods: We perform a systematic mapping study to explore the scientific literature on the intersection of sustainable digital transformation, machine learning and software architecture.\u003Cbr>Results: We have found that the intersection of interest is small despite the amount of works on its individual aspects, and not all dimensions of sustainability are represented equally. We also found that application domains are diverse and include many important sectors and industry groups. At the same time, the perceived level of maturity of machine learning adoption by existing works seems to be quite low.\u003Cbr>Conclusion: Our findings show an opportunity for further software architecture research to aid sustainable digital transformation, especially by building on the emerging practice of machine learning operations. |\n\nContents  \n1. Introduction ...................................................................................................................................................................................................... 2  \n2. Background ....................................................................................................................................................................................................... 2  \n2.1. Disambiguation of concepts ..................................................................................................................................................................... 2  \n2.2. Machine learning and software architecture.............................................................................................................................................. 3  \n2.3. Sustainability and software architecture ................................................................................................................................................... 3  \n2.4. Sustainability and ML/MLOps.................................................................................................................................................................. 3  \n3. Study design...................................................................................................................................................................................................... 3  \n3.1. Choice of methodology ........................................................................................................................................................................... 3  \n3.2. Definition of research questions .........","cbCaidzQUw5ZZ7Ei","https://ap.wps.com/l/cbCaidzQUw5ZZ7Ei","pdf",1681999,1,13,"English","en",105,"# 1. Introduction\n# 2. Background\n## 2.1. Disambiguation of concepts\n## 2.2. Machine learning and software architecture\n## 2.3. Sustainability and software architecture\n## 2.4. Sustainability and ML/MLOps\n# 3. Study design\n## 3.1. Choice of methodology\n## 3.2. Definition of research questions\n## 3.3. Building the search query\n## 3.4. Filtering bibliographical results\n## 3.5. Data extraction\n# 4. Findings\n## 4.1. RQ1: Intersection of SDT with ML and SA\n## 4.2. RQ2: Sustainability dimensions\n## 4.3. RQ3: Application domains\n## 4.4. RQ4: Architecture components for MLOps","[{\"question\":\"What is the main objective of the systematic mapping study?\",\"answer\":\"To clarify how sustainable digital transformation, software architecture, and machine learning interact, and which considerations from software architecture apply in this context.\"},{\"question\":\"How was the study conducted?\",\"answer\":\"By performing a systematic mapping study that explores scientific literature at the intersection of sustainable digital transformation, machine learning, and software architecture.\"},{\"question\":\"What were the key findings about the intersection of these topics?\",\"answer\":\"The intersection is small, sustainability dimensions are not represented equally, application domains are diverse, and the maturity of machine learning adoption appears low in existing works.\"}]","Software architecture for machine learning to aid sustainable digital transformation - A systematic mapping study | 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is the main objective of the systematic mapping study?","Question",{"text":75,"@type":76},"To clarify how sustainable digital transformation, software architecture, and machine learning interact, and which considerations from software architecture apply in this context.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the study conducted?",{"text":80,"@type":76},"By performing a systematic mapping study that explores scientific literature at the intersection of sustainable digital transformation, machine learning, and software architecture.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the key findings about the intersection of these topics?",{"text":84,"@type":76},"The intersection is small, sustainability dimensions are not represented equally, application domains are diverse, and the maturity of machine learning adoption appears low in existing 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