[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123633-en":3,"doc-seo-123633-105":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},123633,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Scaling Data Science Solutions with Semantics and Machine Learning - Bosch Case","Industry 4.0 and IoT enable factories to generate massive, heterogeneous data, creating big data challenges in both volume and variety. Cloud and distributed systems can parallelize processing and reduce training and runtime, but onboarding non-cloud users remains costly and time-consuming. SemCloud addresses this by combining domain ontologies and mappings with distributed semantic integration and analysis, adaptive Datalog rules, and machine learning for automated resource configuration. Evaluations in a Bosch welding quality monitoring case use millions of records and thousands of repeated runs, delivering promising results.","arXiv :2308 .0 1094v 1 [ cs .AI] 2 Aug 2023  \nScaling 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)  \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 whole process involves iterative and cross-domain communication between the stakeholders (f) .  \nChallenges. From the scenario, we see that scaling data science solutions poses challenges related to dealing with the high data volume, variety, and more users, namely enabling non-cloud experts to leverage cloud systems. Indeed, industries equipped with IoT technologies produce huge volumes o","cbCaip97oyyPdyYB","https://ap.wps.com/l/cbCaip97oyyPdyYB","pdf",1674523,1,19,"English","en",105,"# Introduction\n## Background\n## Challenges\n## Our Approach\n## SemCloud Components","[{\"question\":\"What problem does SemCloud address in Industry 4.0 data science projects?\",\"answer\":\"SemCloud targets the difficulty of scaling data science solutions on cloud systems for non-cloud experts while handling large data volume and high data variety common in factories.\"},{\"question\":\"How does SemCloud integrate semantic technologies with distributed cloud computing?\",\"answer\":\"SemCloud uses domain ontologies and mappings to support semantic data integration and parallelizes semantic data integration and analysis across distributed computing nodes.\"},{\"question\":\"What automation enables non-cloud experts to deploy solutions on the cloud?\",\"answer\":\"SemCloud employs adaptive Datalog rules together with machine learning to automate resource configuration, reducing reliance on cloud experts for setup and changes.\"}]","Scaling Data Science Solutions with Semantics and Machine Learning - Bosch Case | PDF",1785817741,48,{"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},"scaling-data-science-solutions-with-semantics-and-machine-learning-bosch-case","",{"@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/scaling-data-science-solutions-with-semantics-and-machine-learning-bosch-case/123633/",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 problem does SemCloud address in Industry 4.0 data science projects?","Question",{"text":75,"@type":76},"SemCloud targets the difficulty of scaling data science solutions on cloud systems for non-cloud experts while handling large data volume and high data variety common in factories.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SemCloud integrate semantic technologies with distributed cloud computing?",{"text":80,"@type":76},"SemCloud uses domain ontologies and mappings to support semantic data integration and parallelizes semantic data integration and analysis across distributed computing nodes.",{"name":82,"@type":73,"acceptedAnswer":83},"What automation enables non-cloud experts to deploy solutions on the cloud?",{"text":84,"@type":76},"SemCloud employs adaptive Datalog rules together with machine learning to automate resource configuration, reducing reliance on cloud experts for setup and changes.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]