[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117823-en":3,"doc-seo-117823-105":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},117823,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML","Systems engineering provides an integrative way to design and manage complex systems, while cyber-physical systems (CPS) generate data that must be extracted, prepared, transformed, and interpreted across engineering and data-science workflows. The proposed approach introduces collaborative model-based engineering in SysML to define machine-learning tasks, connect semantic data attributes, integrate heterogeneous data sources, and specify data-processing steps. It is assessed using smart-weather and 3D-printer waste-prevention scenarios, supported by a user study on workload and usability.","arXiv :2307 .04495v 1 [ cs . SE] 10 Jul 2023  \nModel-Driven Engineering Method to Support the Formalization of Machine Learning using SysML  \nSimon Raedler 1,2*, Juergen Mangler 1 and Stefanie Rinderle-Ma 1  \n1 TUM School of Computation, Information and Technology;  \nDepartment of Computer Science, Technical University of Munich, Boltzmannstra􀀙e 3, Garching b. M􀁿unchen, 85748, Germany.  \n2 Business Informatics Group, Technical University of Vienna,  \nFavoritenstra􀀙e 9-11/194-3, Vienna, 1040, Austria.  \n*Corresponding author(s). E-mail(s): [simon.raedler@tum.de](simon.raedler@tum.de) ; Contributing [authors: juergen.mangler@tum.de](authors: juergen.mangler@tum.de);  \nstefanie.rinderle-ma@tum.de;  \nAbstract  \nMotivation: Systems Engineering is a transdisciplinary and integrative approach, that enables the design, integration, and management of complex systems in systems engineering life cycles. In order to use data generated by cyber-physical systems (CPS), systems engineers cooperate with data scientists, to develop customized mechanisms for data extraction, data preparation, and/or data transformation. While interfaces in CPS systems may be generic, data generated for custom applications must be transformed and merged in speci􀀌c ways so that insights into the data can be interpreted by system engineers or dedicated applications to gain additional insights. To foster e􀀎 -cient cooperation between systems engineers and data scientists, the systems engineers have to provide a 􀀌ne-grained speci􀀌cation that describes (a) all parts of the CPS,(b) how the CPS might interact, (c) what data is exchanged between them,(d) how the data interrelates, and (e) what are the requirements and goals of the data extraction. A data scientist can then iteratively  \n1  \n(including further re􀀌nements of the speci􀀌cation) prepare the necessary custom machine-learning models and components.  \nMethods: This work introduces a method supporting the collaborative de􀀌nition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identi-􀀌cation and integration of various data sources, the required de􀀌nition of semantic connections between data attributes, and the de􀀌nition of data processing steps within the machine learning support.  \nResults: By consolidating the knowledge of domain and machine learning experts, a powerful tool to describe machine learning tasks by formalizing knowledge using the systems modeling language SysML is introduced. The method is evaluated based on two use cases, i.e., a smart weather system that allows to predict weather forecasts based on sensor data, and a waste prevention case for 3D printer 􀀌lament that cancels the printing if the intended result cannot be achieved (image processing) .  \nFurther, a user study is conducted to gather insights of potential users regarding perceived workload and usability of the elaborated method.  \nConclusion: Integrating machine learning-speci􀀌c properties in systems engineering techniques allows non-data scientists to understand formalized knowledge and de􀀌ne speci􀀌c aspects of a machine learning problem, document knowledge on the data, and to further support data scientists to use the formalized knowledge as input for an implementation using (semi-) automatic code generation. In this respect, this work contributes by consolidating knowledge from various domains and therefore, fosters the integration of machine learning in industry by involving several stakeholders.  \nKeywords: Model-Driven Engineering, SysML, Systems Engineering, Machine  \nLearning, Knowledge Formalization, Data-Driven Engineering, PLM, MDE4AI  \nAcknowledgments. This project has been partially supported and funded by the Austrian Research Promotion Agency (FFG) via the Austrian Competence Center for Digital Production (CDP) under the contract number 881843 .  \n2  \n1 Introduction  \nLeveraging data to allow experts making informed deci","cbCaii7NcNiqrSQ7","https://ap.wps.com/l/cbCaii7NcNiqrSQ7","pdf",1650864,1,43,"English","en",105,"# Abstract\n# 1 Introduction\n## Data-driven engineering and knowledge requirements\n## Related work and limitations\n## Goals and proposed approach","[{\"question\":\"Why is SysML used to support formalizing machine learning tasks?\",\"answer\":\"SysML is leveraged for model-based engineering to formalize knowledge from multiple viewpoints, enabling engineers to define machine learning tasks and document data aspects in a structured way.\"},{\"question\":\"What elements does the method help specify for machine learning integration?\",\"answer\":\"The method supports identifying and integrating data sources, defining semantic connections between data attributes, and specifying data processing steps within machine-learning support.\"},{\"question\":\"How is the method evaluated in the paper?\",\"answer\":\"Evaluation is based on two use cases: a smart weather system for sensor-driven forecasting and a 3D-printer waste-prevention scenario that cancels printing when targets cannot be achieved, plus a user study measuring workload and usability.\"}]","Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML | 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