{"title":"An MDA approach for robotic-based real-time business intelligence applications","authors":"Houssam Bazza , Sandro Bimonte , Zakaria Gourti , Stefano Rizzi , Hassan Badir","doi":"10.1016/j.datak.2025.102418","DOIUrl":null,"url":null,"abstract":"<div><div>Industry 4.0, the fourth industrial revolution, has emerged from the convergence of robotics, automation, and the Internet of Things (IoT), transforming industrial processes with intelligent systems and digital integration. This revolution also brings with it Business Intelligence (BI) systems that enable the analysis of IoT and robotic data. The data architectures employed for BI in Industry 4.0 contexts are often intricate, typically comprising robots software, DBMSs, message brokers, and data stream management systems. Consequently, designing BI data-centric applications for Industry 4.0 presents a significant challenge. Inspired by the absence of modeling approaches for this type of application and by the well-established advantages of Model-Driven Architecture (MDA), this paper introduces a novel UML profile for real-time robotic data-driven BI applications. Our profile enables the representation of robotic and transactional data within a unified and consistent framework, enabling continuous queries over these streams. Additionally, we propose an automated method to implement UML class diagrams onto a technological stack featuring ROS, Apache Kafka, PostgreSQL, and Apache Flink. An experimental evaluation in the agricultural application domain confirms the merits of our approach.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"157 ","pages":"Article 102418"},"PeriodicalIF":2.7000,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data & Knowledge Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169023X25000138","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Industry 4.0, the fourth industrial revolution, has emerged from the convergence of robotics, automation, and the Internet of Things (IoT), transforming industrial processes with intelligent systems and digital integration. This revolution also brings with it Business Intelligence (BI) systems that enable the analysis of IoT and robotic data. The data architectures employed for BI in Industry 4.0 contexts are often intricate, typically comprising robots software, DBMSs, message brokers, and data stream management systems. Consequently, designing BI data-centric applications for Industry 4.0 presents a significant challenge. Inspired by the absence of modeling approaches for this type of application and by the well-established advantages of Model-Driven Architecture (MDA), this paper introduces a novel UML profile for real-time robotic data-driven BI applications. Our profile enables the representation of robotic and transactional data within a unified and consistent framework, enabling continuous queries over these streams. Additionally, we propose an automated method to implement UML class diagrams onto a technological stack featuring ROS, Apache Kafka, PostgreSQL, and Apache Flink. An experimental evaluation in the agricultural application domain confirms the merits of our approach.
期刊介绍:
Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.