[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123790-en":3,"doc-seo-123790-105":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},123790,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Building Domain-Specific Machine Learning Workflows - A Conceptual Framework for the State-of-the-Practice","Domain experts increasingly apply machine learning to address domain-specific problems, yet translating expertise into executable computational workflows remains challenging. This article introduces a conceptual framework capturing the route of transformations a domain expert may take, and identifies six key challenges faced when moving from problem definition to implementable workflow systems. It grounds the framework in the current state-of-the-practice by examining textual and graphical workflow systems, classifying tool support and domain-specificity, and highlighting gaps in automation and transformation coverage, while proposing research directions to bridge software engineering and scientific domains.","Titre:  Building Domain-Specific Machine Learning Workflows: A Title:  Conceptual Framework for the State-of-the-Practice  \nAuteurs:   \n Bentley Oakes, Michalis Famelis, & Houari Sahraoui  \n Authors:   Date:  2024   Type:  Article de revue / Article   \n Oakes, B. , Famelis, M. , & Sahraoui, H. (2024) . Building Domain-Specific Machine  \nRéférence:  Learning Workflows: A Conceptual Framework for the State-of-the-Practice. ACM Citation: Transactions on Software Engineering and Methodology, 33(4), 1-50.  \n  [https://doi.org/10.1145/3638243](https://doi.org/10.1145/3638243)   \n| Document en libre accès dans PolyPublie\u003Cbr>Open Access document in PolyPublie\u003Cbr> |  |  |  |\n| --- | --- | --- | --- |\n| URL de PolyPublie:\u003Cbr>PolyPublie URL: | [https://publications.polymtl.ca/57010/](https://publications.polymtl.ca/57010/) |  |  |\n| Version: | Version finale avant publication / Accepted version Révisé par les pairs / Refereed |  |  |\n| Conditions d’utilisation: \u003Cbr> Tous droits réservés / All rights reserved\u003Cbr>Terms of Use:\u003Cbr> |  |  |  |\n\n\n| Document publié chez l’éditeur officiel\u003Cbr>Document issued by the official publisher\u003Cbr> |\n| --- |\n| Titre de la revue:  ACM Transactions on Software Engineering and Methodology (vol. 33, Journal Title:  no. 4)\u003Cbr>Maison d’édition:\u003Cbr>  Publisher:  Association for Computing Machinery      URL officiel:\u003Cbr> Official URL:  [https://doi.org/10.1145/3638243](https://doi.org/10.1145/3638243)      \u003Cbr>Mention légale:  © 2024 Copyright held by the owner/author(s) . Publication rights licensed to ACM.\u003Cbr>  Legal notice:               \u003Cbr> |\n\nCe fichier a été téléchargé à partir de PolyPublie, le dépôt institutionnel de Polytechnique Montréal  \nThis file has been downloaded from PolyPublie, the institutional repository of Polytechnique Montréal  \n[https://publications.polymtl.ca](https://publications.polymtl.ca)  \nBuilding Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice  \nBENTLEY JAMES OAKES, Département de génie informatique et génie logiciel, Polytechnique Montréeal, Canada MICHALIS FAMELIS, Département d’informatique et de recherche opérationnelle, Université de Montréal, Canada HOUARI SAHRAOUI, Département d’informatique et de recherche opérationnelle, Université de Montréal, Canada  \nDomain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation. These challenges arise out of our conceptual framework which presents the “route” of transformations that a domain expert may choose to take while developing their solution.  \nTo ground our conceptual framework in the state-of-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for the transformations described in our framework. Example studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation.  \nThe state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges and transformations are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.  \nCCS Concepts: • Computing methodologies → Machine learning; Knowledge representation and reasoning.  \nAdditional Key Words and Phrases: computational workflow, workflow composition, domain experts, machine learning, machine learning pipelines, software engineering framework  \nACM Reference Format:  \nBentley James Oakes, Michal","cbCaibzgB7qtedsm","https://ap.wps.com/l/cbCaibzgB7qtedsm","pdf",2792854,1,50,"English","en",105,"# Introduction\n## Conceptual framework and route of transformations\n## Six key challenges in domain expert workflows\n## State-of-the-practice workflow systems and support\n## Research directions and automation of tools","[{\"question\":\"What does the article propose for building domain-specific machine learning workflows?\",\"answer\":\"It proposes a conceptual framework that models the route of transformations a domain expert can take when developing a computational workflow and its executable implementation.\"},{\"question\":\"What are the six key challenges discussed in the paper?\",\"answer\":\"The paper identifies six key challenges a domain expert faces in turning their problem into a computational workflow, and it examines which challenges and transformations existing tools do not sufficiently address.\"},{\"question\":\"How does the paper evaluate the state-of-the-practice?\",\"answer\":\"It reviews and analyzes available textual and graphical workflow systems, studies examples from the literature across domains, and uses these to assess tool support and categorize domain-specificity and machine learning usage.\"}]","Building Domain-Specific Machine Learning Workflows - A Conceptual Framework for the State-of-the-Practice | PDF",1785818583,126,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"building-domain-specific-machine-learning-workflows-a-conceptual-framework-for-the-state-of-the-practice","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/building-domain-specific-machine-learning-workflows-a-conceptual-framework-for-the-state-of-the-practice/123790/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the article propose for building domain-specific machine learning workflows?","Question",{"text":75,"@type":76},"It proposes a conceptual framework that models the route of transformations a domain expert can take when developing a computational workflow and its executable implementation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the six key challenges discussed in the paper?",{"text":80,"@type":76},"The paper identifies six key challenges a domain expert faces in turning their problem into a computational workflow, and it examines which challenges and transformations existing tools do not sufficiently address.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper evaluate the state-of-the-practice?",{"text":84,"@type":76},"It reviews and analyzes available textual and graphical workflow systems, studies examples from the literature across domains, and uses these to assess tool support and categorize domain-specificity and machine learning usage.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":21,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]