Ernesto Exposito

Nicolas Evain, Ernesto Exposito, Philippe Arnould

Despite comprehensive Bodies of Knowledge (BoKs) documenting core knowledge across software engineering, computer science, information systems, and emerging computing fields, a critical gap persists: methodologies for integrating this knowledge into coherent competency-based curricula that prepare graduates for professional careers remain underdeveloped. This paper presents a competency-mapping methodology that bridges Bodies of Knowledge and competency frameworks to design computing curricula. We demonstrate this methodology through ISANUM, a five-year engineering degree program featuring 23 competencies organized into five thematic blocks, each with explicit mappings to 494 knowledge topics from 34 Computing Knowledge areas defined in Computing Curricula 2020. The program integrates three specialized pathways (Software Engineering, Data Engineering \& Data Science, and Information Technology) with mandatory work-study programs, ensuring graduates develop both theoretical foundations and practical workplace competencies. Our contribution provides computing educators with a replicable methodology for translating Bodies of Knowledge into assessable competency frameworks, supported by a semantic wiki infrastructure (ISANUMpedia) enabling collaborative curriculum understanding, maintenance and evolution.

Rafael Cestari, Sebastien Ducos, Ernesto Exposito

Current automation approaches in the Industry 4.0 have generated increased interest in the utilization of Integration Platforms as a Service (iPaaS) cloud architectures in order to unify and synchronize several systems, applications, and services in order to build smart solutions for automated and adaptive industrial process management. Existing iPaaS solutions present several out-of-the-box connectors and automation engines for easier integration of customers' projects, but show issues regarding overall adaptation outside their scope, brand locking, and occasionally high prices. Moreover, existing platforms fail to respond adequately to the needs of deploying multiple decision models capable of offering automated or semi-automated management of processes, thanks to the integration of the large diversity of data and event sources as well as the different physical or logical action entities. With the popularization of open-source software and applications such as BPM Engines, Machine Learning libraries, and Integration suites and libraries, it is possible to develop a fully customizable and adaptable, open-source iPaaS that can be used both in and outside industrial applications. In this paper, we propose a generic iPaaS architecture implemented on the basis of several open source solutions boasting integration, interoperability, and automated decision-making capabilities in the domain of Agriculture 4.0. A proof-of-concept based on these solutions is presented, as well as a case study on MA{Ï}SADOUR's grain storage process with a comparison with the currently human-operated tasks.