[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124087-en":3,"doc-seo-124087-105":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},124087,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Scaling Data Science Solutions with Semantics and Machine Learning - Bosch Case","Industry 4.0 and IoT technologies generate large volumes and diverse data from factory production, creating big data challenges across volume and variety. Distributed cloud infrastructures can parallelize processing, but onboarding non-cloud experts such as data and domain scientists to train and deploy solutions remains time-consuming and difficult. SemCloud is a semantics-enhanced cloud system integrating domain ontologies and mappings with machine learning and adaptive Datalog rules. Evaluated on an industrial Bosch welding scenario with millions of records and thousands of runs, it delivers promising results for automated configuration and scalable semantic ETL and analysis.","Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case  \nBaifan Zhou 1 ,2 , ∗ , Nikolay Nikolov3 , 1 ,∗ , Zhuoxun Zheng4 , 1 , Xianghui Luo5 ,  \nOgnjen Savkovic6 , Dumitru Roman3 , 1 , Ahmet Soylu2 , and  \nEvgeny Kharlamov4 , 1  \n1 Department of Informatics, University of Oslo, Norway  \n2 Department of Computer Science, Oslo Metropolitan University, Norway  \n3 SINTEF AS, Norway  \n4 Bosch Center for Artificial Intelligence, Germany  \n5 ACM Member, Germany  \n6 Department of Computer Science, Free University of Bozen-Bolzano, Italy  \nAbstract. Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is nontrivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelisesthe semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing noncloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.  \nKeywords: ontology engineering · knowledge graph · semantic ETL · machine learning · cloud computing · welding · quality monitoring · Industry 4.0 · rule-based reasoning · Datalog  \n1 Introduction  \nBackground. Industry 4.0 [1] aims at highly automated smart factories that rely on IoT technology [2], spanning across data acquisition, communication, information processing and actuation. This has unlocked unprecedented amounts  \n∗ Baifan Zhou and Nikolay Nikolov contributed equally to this work as first authors. [baifanz@ifi.uio.no](baifanz@ifi.uio.no) , [nikolay.nikolov@sintef.no](nikolay.nikolov@sintef.no)  \nThis is a post-peer-review, pre-copyedit version of the following conference proceeding: Zhou, [B. et](B. et) al. (2023) . Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case. In: Payne, T. R., et al. The Semantic Web – ISWC 2023.  \nISWC 2023. Lecture Notes in Computer Science, vol 14266. Springer, Cham.  \nDOI: [https://doi.org/10.1007/978-3-031-47243-5_21](https://doi.org/10.1007/978-3-031-47243-5_21)  \n2 Zhou and Nikolov, et al.  \nFig. 1. Data analytics development cycle exemplified on the Bosch case of welding condition monitoring. In industrial data science projects, many users are non-cloud experts (e.g., welding experts, ML experts) and want to scale their solutions on the cloud.  \nof data that are generated by production systems [3] and, thus, drastically increased the demand for data-driven analytical solutions and cloud technology. We illustrate a common industrial scenario of development and deployment of data-driven solutions on cloud with a Bosch welding case7 of quality monitoring in Fig. 1: The data from a production environment such as welding machines (a) has first to be acquired in different formats, e.g., CSV, JSON, XML (b); then they should be integrated into a uniform format (c); After that, the project team (including welding experts, data scientists, managers. etc.) wants to run data analysis on cloud infrastructures on top of the large data volumes from many factories (d); After data analysis, these users need to discuss and log the results (e); The who","cbCaillzDlFb9Q7a","https://ap.wps.com/l/cbCaillzDlFb9Q7a","pdf",1637795,1,19,"English","en",105,"# Introduction\n## Background\n## Challenges\n## Our Approach","[{\"question\":\"What problem does SemCloud address in Industry 4.0 data analytics?\",\"answer\":\"SemCloud targets the difficulty of scaling data science solutions on cloud platforms when data volumes and formats are large and many users are not cloud experts.\"},{\"question\":\"How does SemCloud integrate semantics into cloud-based data science?\",\"answer\":\"It uses domain ontologies and mappings for data integration, parallelizing semantic data integration and data analysis across distributed computing nodes.\"},{\"question\":\"What automation does SemCloud provide for non-cloud users?\",\"answer\":\"SemCloud adopts adaptive Datalog rules and machine learning to automate resource configuration, enabling domain and other non-cloud experts to use the cloud system effectively.\"}]","Scaling Data Science Solutions with Semantics and Machine Learning - 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