[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-585330-105":59,"doc-detail-585330-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","model-assisted-statistics-with-mobile-web-usage-practical-implementation-of-knime-analytics-platform-and-knime-pro-hub","MODEL ASSISTED STATISTICS WITH MOBILE WEB USAGE - Practical Implementation of KNIME Analytics Platform and KNIME Pro Hub","","This paper presents a practical implementation for performing data analytics and statistics based on mobile web usage. It uses the KNIME Analytics Platform together with KNIME Pro Hub to build analytical workflows, deploy them to the cloud, and make them accessible from mobile devices. Statistical learning model processing runs on KNIME Pro infrastructure to ensure real-time execution without relying on device computation. Users access instant results, metrics, and visualizations through a mobile web interface, demonstrating KNIME as a scalable low-code cloud solution for interactive learning analytics.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/model-assisted-statistics-with-mobile-web-usage-practical-implementation-of-knime-analytics-platform-and-knime-pro-hub/585330/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/model-assisted-statistics-with-mobile-web-usage-practical-implementation-of-knime-analytics-platform-and-knime-pro-hub/585330.png","ImageObject",300,407,{"name":92,"@type":93},"Aria Callaghan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-11","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the proposed approach?","Question",{"text":112,"@type":113},"To implement real-time data analytics and statistical modelling for mobile web usage by building and deploying KNIME workflows in the cloud and accessing them through mobile devices.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the solution handle computation for statistical learning models?",{"text":117,"@type":113},"All processing tasks related to statistical learning models are performed on the KNIME Pro infrastructure, avoiding the need for computational power on the mobile device.",{"name":119,"@type":110,"acceptedAnswer":120},"What benefits does the paper claim for using KNIME?",{"text":121,"@type":113},"It highlights KNIME as a low-code, cloud-based platform that enables efficient, scalable, and interactive statistical analysis, including instant access to results, metrics, and visualizations via a mobile web interface.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},585330,1792756268,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},962084926284,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","MODEL ASSISTED STATISTICS WITH MOBILE WEB  \nUSAGE  \nIoannis Leftheris 1, Theodoros Xanthopoulos2 and Kyriaki Tsilika3  \nDepartment of Economics, University of Thessaly  \n1Kavakli 15, Larissa, P. C. 41335 Greece  \n2Lemessou 34, Larissa, P. C. 41335 Greece  \n328hs Octovriou 78, Volos, P. C. 38333 Greece  \nABSTRACT  \nThis paper presents a practical implementation of doing data analytics and statistics with mobile web usage using the KNIME Analytics Platform and KNIME Pro Hub. The approach demonstrates how analytical workflows can be developed on the KNIME Analytics Platform, deployed to the cloud, and accessed or executed through mobile devices while all processing tasks related to statistical learning models are performed on the KNIME Pro infrastructure, ensuring real-time execution without requiring computational power from the mobile device. Users interact with the analytical environment through a mobile web interface, retrieving results, metrics, and visualizations instantly. This setup highlights the potential of KNIME as a low-code, cloud-based solution for mobile data analytics, enabling efficient, scalable, and interactive statistical analysis on the web.  \nKEYWORDS  \nLearning Analytics, Mobile Learning, Cloud Computing, Real-Time Analytics, and KNIME Pro Hub  \n1. INTRODUCTION  \nThe exponential growth of mobile web usage has generated large-scale datasets. These datasets enable the systematic analysis of user behavior, interaction patterns, and digital habits, particularly when combined with scalable data analytics infrastructures. Despite the increasing availability of such data, performing real-time analytics and statistical modelling directly on mobile devices, remains technically challenging. This is due to limited processing capacity and the absence of specialized tools that support cloud-based execution (Giraldo-Jiménez, 2022) . This has led to a growing need for scalable, cloud-based infrastructures that support efficient statistical analysis and data analytics through web-enabled platforms, particularly in scenarios where access via mobile devices is required.  \nLow-code analytical environments, such as the KNIME Analytics Platform, have recently emerged as powerful solutions for implementing data processing and machine learning workflows without requiring extensive programming expertise for data preprocessing, statistical analysis, or machine learning model development (Berthold et al., 2009) . When deployed through the KNIME Pro hub, these workflows operate entirely in the cloud, allowing users to access analytical results, visualizations, and predictive models in realtime from any mobile browser. This approach eliminates the computational limitations of mobile devices while maintaining analytical precision and reproducibility (Lee et al., 2025) .  \nThe integration of cloud-based workflow management with mobile accessibility redefines how data analysis and statistical computation can be delivered in modern educational and professional contexts. According to Kabassi and Alepis (2019), learning analytics systems that exploit mobile and distance learning technologies can provide customised feedback to users. Extending this idea to practical data analytics, the present work demonstrates how KNIME Pro and KNIME Hub can operationalize machine learning and statistical methods using mobile web usage, validating the quality and functionality of several features of KNIME technologies.  \nTherefore, this paper constitutes a software review article to assess the functionality, usability, performance, and compliance of KNIME Pro and KNIME Hub with mobile learning requirements. The paper focuses on designing and deploying KNIME-based workflows that execute statistical and machine learning operations entirely in the cloud and are accessible through mobile interfaces. The proposed approach helps bridge the gap between real-time analytics and mobile accessibility, establishing KNIME as a scalable, low-code, and educationally rele","cbCaitRQyx1MbPiw","https://ap.wps.com/l/cbCaitRQyx1MbPiw","pdf",1326017,"English","# Abstract\n# 1. Introduction\n## Problem and motivation\n## Low-code approach with KNIME\n## Educational and professional relevance\n# 2. Related Work\n## Mobile data analytics trends\n## Cloud-based immersive mobile learning\n## Low-code platforms and KNIME deployment\n# 3. Method/Implementation (start)","[{\"question\":\"What is the main goal of the proposed approach?\",\"answer\":\"To implement real-time data analytics and statistical modelling for mobile web usage by building and deploying KNIME workflows in the cloud and accessing them through mobile devices.\"},{\"question\":\"How does the solution handle computation for statistical learning models?\",\"answer\":\"All processing tasks related to statistical learning models are performed on the KNIME Pro infrastructure, avoiding the need for computational power on the mobile device.\"},{\"question\":\"What benefits does the paper claim for using KNIME?\",\"answer\":\"It highlights KNIME as a low-code, cloud-based platform that enables efficient, scalable, and interactive statistical analysis, including instant access to results, metrics, and visualizations via a mobile web interface.\"}]","MODEL ASSISTED STATISTICS WITH MOBILE WEB USAGE - Practical Implementation of KNIME Analytics Platform and KNIME Pro Hub | PDF",1792054395,25]