The IoT-Sim is a lightweight and modular tool designed to create, configure, and test models that detect attacks in Internet of Things (IoT) networks. It provides an interactive environment for simulating communication among connected devices and evaluating intrusion detection models. This framework allows researchers to design network topologies, inject different types of attacks, and benchmark detection algorithms under controlled conditions. By combining usability and flexibility in an open-source design, the simulator is a valuable resource for the education, research, and rapid prototyping of IoT security solutions.
{"title":"IoT-Sim: An interactive platform for designing and securing smart device networks","authors":"Alejandro Diez Bermejo, Branly Martinez Gonzalez, Beatriz Gil-Arroyo, Jaime Rincón Arango, Daniel Urda Muñoz","doi":"10.1016/j.softx.2026.102527","DOIUrl":"10.1016/j.softx.2026.102527","url":null,"abstract":"<div><div>The IoT-Sim is a lightweight and modular tool designed to create, configure, and test models that detect attacks in Internet of Things (IoT) networks. It provides an interactive environment for simulating communication among connected devices and evaluating intrusion detection models. This framework allows researchers to design network topologies, inject different types of attacks, and benchmark detection algorithms under controlled conditions. By combining usability and flexibility in an open-source design, the simulator is a valuable resource for the education, research, and rapid prototyping of IoT security solutions.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102527"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-30DOI: 10.1016/j.softx.2025.102461
Shayan Tohidi, Sigurdur Olafsson
Stochastic dominance is a classical method for comparing two random variables using their probability distribution functions. As for all stochastic orders, stochastic dominance does not always establish an order between the random variables, and almost stochastic dominance was developed to address such cases, thus extending the applicability of stochastic dominance to many real-world problems. We developed an R package that consists of a collection of methods for testing the first- and second-order (almost) stochastic dominance for discrete random variables. This article describes the package and illustrates these methods using both synthetic datasets covering a range of possible scenarios that can occur, and a practical example where the comparison of discrete random variables using stochastic dominance can be applied to aid decision-making.
{"title":"RSD: An R package to calculate stochastic dominance","authors":"Shayan Tohidi, Sigurdur Olafsson","doi":"10.1016/j.softx.2025.102461","DOIUrl":"10.1016/j.softx.2025.102461","url":null,"abstract":"<div><div>Stochastic dominance is a classical method for comparing two random variables using their probability distribution functions. As for all stochastic orders, stochastic dominance does not always establish an order between the random variables, and almost stochastic dominance was developed to address such cases, thus extending the applicability of stochastic dominance to many real-world problems. We developed an R package that consists of a collection of methods for testing the first- and second-order (almost) stochastic dominance for discrete random variables. This article describes the package and illustrates these methods using both synthetic datasets covering a range of possible scenarios that can occur, and a practical example where the comparison of discrete random variables using stochastic dominance can be applied to aid decision-making.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102461"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-10DOI: 10.1016/j.softx.2026.102511
Aulia Arif Wardana , Grzegorz Kołaczek , Parman Sukarno
This research introduces CIDS-Sim, a simulator for Collaborative Intrusion Detection Systems (CIDS) based on federated learning, addressing the complexity of coordinated attacks on networks. Traditional Intrusion Detection Systems (IDS) are limited by isolated operations and privacy concerns. CIDS-Sim leverages federated learning to maintain data privacy while enabling collaborative anomaly detection. It assesses collaboration strategies, federated learning’s privacy-performance trade-offs, and different attack vectors and defenses. CIDS-Sim is a critical tool for researchers and practitioners to develop secure IDS solutions, offering a robust platform for simulating and evaluating the dynamics of collaborative defense strategies. CIDS-Sim is also suitable for educators or lecturers who want to teach the concept of CIDS.
{"title":"CIDS-Sim: Simulator for collaborative intrusion detection system based on federated learning","authors":"Aulia Arif Wardana , Grzegorz Kołaczek , Parman Sukarno","doi":"10.1016/j.softx.2026.102511","DOIUrl":"10.1016/j.softx.2026.102511","url":null,"abstract":"<div><div>This research introduces CIDS-Sim, a simulator for Collaborative Intrusion Detection Systems (CIDS) based on federated learning, addressing the complexity of coordinated attacks on networks. Traditional Intrusion Detection Systems (IDS) are limited by isolated operations and privacy concerns. CIDS-Sim leverages federated learning to maintain data privacy while enabling collaborative anomaly detection. It assesses collaboration strategies, federated learning’s privacy-performance trade-offs, and different attack vectors and defenses. CIDS-Sim is a critical tool for researchers and practitioners to develop secure IDS solutions, offering a robust platform for simulating and evaluating the dynamics of collaborative defense strategies. CIDS-Sim is also suitable for educators or lecturers who want to teach the concept of CIDS.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102511"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-12-03DOI: 10.1016/j.softx.2025.102471
Jeonghwan Kim , Hyunsoo Kim , Jeongku Yun , Jinkyoung Kim , Kwanjung Yee
As the demand for unmanned vehicle (UV) systems continues to grow across a wide range of industries, there is an increasing need for professionals equipped to carry out mission-specific, system-level design. However, traditional engineering education often lacks structured methods for system-level design and does not provide adequate environments for hands-on, collaborative design experiences. To address this gap, we present the Comprehensive Design Framework for Advanced Mobility (CoDeF)—a web-based collaborative platform tailored for early-stage UV system design and education. Built on systems engineering principles, CoDeF provides a structured design process and supports synchronized collaboration among multiple users through shared data and workflows. The platform offers high extensibility and configurability, allowing instructors to flexibly modify design stages and deliverables to meet specific educational objectives. CoDeF has been successfully implemented in multiple university courses, demonstrating its potential as a practical tool for bridging the gap between academic training and industry-oriented system design practice.
{"title":"CoDeF: A web-based education platform for system-level design of unmanned vehicles","authors":"Jeonghwan Kim , Hyunsoo Kim , Jeongku Yun , Jinkyoung Kim , Kwanjung Yee","doi":"10.1016/j.softx.2025.102471","DOIUrl":"10.1016/j.softx.2025.102471","url":null,"abstract":"<div><div>As the demand for unmanned vehicle (UV) systems continues to grow across a wide range of industries, there is an increasing need for professionals equipped to carry out mission-specific, system-level design. However, traditional engineering education often lacks structured methods for system-level design and does not provide adequate environments for hands-on, collaborative design experiences. To address this gap, we present the Comprehensive Design Framework for Advanced Mobility (CoDeF)—a web-based collaborative platform tailored for early-stage UV system design and education. Built on systems engineering principles, CoDeF provides a structured design process and supports synchronized collaboration among multiple users through shared data and workflows. The platform offers high extensibility and configurability, allowing instructors to flexibly modify design stages and deliverables to meet specific educational objectives. CoDeF has been successfully implemented in multiple university courses, demonstrating its potential as a practical tool for bridging the gap between academic training and industry-oriented system design practice.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102471"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-24DOI: 10.1016/j.softx.2026.102532
Dennis Quaresma Pureza, José Luis Vital de Brito, Guilherme Santana Alencar, Luís Augusto Conte Mendes Veloso
This work presents DICLab2D, an open-source digital image correlation (DIC) algorithm developed in the Julia programming language. DICLab2D is a local subset-based 2D DIC code that employs both the inverse compositional Gauss-Newton (IC-GN) and the backward subtractive Gauss-Newton (BS-GN) methods. The algorithm is equipped with shape functions up to the fourth order, reliability-guided displacement tracking, and a dual analysis mode - area and line probes. Standardized tests from the DIC challenge were used to evaluate algorithm performance. The results show that DICLab2D achieves performance equivalent or exceeding that of existing commercial and open-source DIC codes.
{"title":"DICLab2D: An open-source digital image correlation algorithm for Julia language","authors":"Dennis Quaresma Pureza, José Luis Vital de Brito, Guilherme Santana Alencar, Luís Augusto Conte Mendes Veloso","doi":"10.1016/j.softx.2026.102532","DOIUrl":"10.1016/j.softx.2026.102532","url":null,"abstract":"<div><div>This work presents DICLab2D, an open-source digital image correlation (DIC) algorithm developed in the Julia programming language. DICLab2D is a local subset-based 2D DIC code that employs both the inverse compositional Gauss-Newton (IC-GN) and the backward subtractive Gauss-Newton (BS-GN) methods. The algorithm is equipped with shape functions up to the fourth order, reliability-guided displacement tracking, and a dual analysis mode - area and line probes. Standardized tests from the DIC challenge were used to evaluate algorithm performance. The results show that DICLab2D achieves performance equivalent or exceeding that of existing commercial and open-source DIC codes.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102532"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BioIntertidal Mapper 2.0 is a user-friendly tool with a graphical user interface that automates Sentinel-2 processing (since 2017) for intertidal habitat mapping in Google Earth Engine (GEE) using the Normalized Difference Vegetation Index (NDVI). Unlike version 1.0, low-tide scenes are no longer selected using the WorldTides API; instead, images are screened by estimating the proportion of water pixels using the Normalized Difference Water Index. Reflectance inputs were updated to the Sentinel-2 Harmonized dataset. GEE authentication was simplified, the interface refined, and exports expanded to include RGB imagery alongside filtered NDVI products saved to Google Drive. The software enables rapid, reproducible operational mapping for scientists and coastal managers.
{"title":"Updated 2.0 to biointertidal mapper software: A satellite approach for NDVI-based intertidal habitat mapping","authors":"Sara Haro , Ricardo Bermejo , Lara Veylit , Liam Morrison","doi":"10.1016/j.softx.2026.102539","DOIUrl":"10.1016/j.softx.2026.102539","url":null,"abstract":"<div><div>BioIntertidal Mapper 2.0 is a user-friendly tool with a graphical user interface that automates Sentinel-2 processing (since 2017) for intertidal habitat mapping in Google Earth Engine (GEE) using the Normalized Difference Vegetation Index (NDVI). Unlike version 1.0, low-tide scenes are no longer selected using the WorldTides API; instead, images are screened by estimating the proportion of water pixels using the Normalized Difference Water Index. Reflectance inputs were updated to the Sentinel-2 Harmonized dataset. GEE authentication was simplified, the interface refined, and exports expanded to include RGB imagery alongside filtered NDVI products saved to Google Drive. The software enables rapid, reproducible operational mapping for scientists and coastal managers.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102539"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-31DOI: 10.1016/j.softx.2026.102536
Floris den Hengst , Shaad Alaka , Bart A. Kamphorst
The rise of lifestyle-related, non-communicable diseases such as Type II diabetes, cardiovascular diseases, and depression has prompted the development of various behavior change technologies to promote sustained healthy behaviors. User adherence, however, has remained low.
The Collaborative Hybrid Intelligence Platform CHIP is introduced to address adherence challenges by placing the user perspective at the center and facilitating dialogue-based interactions between users and their technical and non-technical support systems—including AI systems, clinicians and caretakers. These interactions aim to uncover barriers to adherence and collaboratively shape personalized lifestyle plans that align with a person’s preferences, values, and context.
CHIP is a microservice-based research platform written in Python with modules implemented as Docker containers. Its modularity allows researchers to replace or adapt specific components, such as natural language reasoners, for technical evaluation and domain-specific adaptation.
{"title":"Collaborative hybrid intelligence platform CHIP: A modular architecture for developing and testing personalized lifestyle support interactions","authors":"Floris den Hengst , Shaad Alaka , Bart A. Kamphorst","doi":"10.1016/j.softx.2026.102536","DOIUrl":"10.1016/j.softx.2026.102536","url":null,"abstract":"<div><div>The rise of lifestyle-related, non-communicable diseases such as Type II diabetes, cardiovascular diseases, and depression has prompted the development of various behavior change technologies to promote sustained healthy behaviors. User adherence, however, has remained low.</div><div>The Collaborative Hybrid Intelligence Platform CHIP is introduced to address adherence challenges by placing the user perspective at the center and facilitating dialogue-based interactions between users and their technical and non-technical support systems—including AI systems, clinicians and caretakers. These interactions aim to uncover barriers to adherence and collaboratively shape personalized lifestyle plans that align with a person’s preferences, values, and context.</div><div>CHIP is a microservice-based research platform written in Python with modules implemented as Docker containers. Its modularity allows researchers to replace or adapt specific components, such as natural language reasoners, for technical evaluation and domain-specific adaptation.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102536"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-28DOI: 10.1016/j.softx.2026.102517
Marcos Jesús Sequera Fernández, Mohammadhossein Homaei, Óscar Mogollón Gutierrez, José Carlos Sancho Núñez
Foruster is a cross-platform desktop application, developed in Rust, for live-system forensic analysis. Unlike traditional tools that require system shutdown, Foruster is designed to identify and catalog files of interest on active storage volumes. Its user interface, built with the Slint framework, guides the analyst through the selection of devices, the configuration of search profiles, and the real-time visualization of results. The software features heuristic detection of anomalies, such as deceptive file extensions, and ensures the integrity of findings through cryptographic hashing, optimizing the digital forensic investigation process.
{"title":"Foruster: A cross-platform tool for live forensic triage and anomaly detection","authors":"Marcos Jesús Sequera Fernández, Mohammadhossein Homaei, Óscar Mogollón Gutierrez, José Carlos Sancho Núñez","doi":"10.1016/j.softx.2026.102517","DOIUrl":"10.1016/j.softx.2026.102517","url":null,"abstract":"<div><div>Foruster is a cross-platform desktop application, developed in Rust, for live-system forensic analysis. Unlike traditional tools that require system shutdown, Foruster is designed to identify and catalog files of interest on active storage volumes. Its user interface, built with the Slint framework, guides the analyst through the selection of devices, the configuration of search profiles, and the real-time visualization of results. The software features heuristic detection of anomalies, such as deceptive file extensions, and ensures the integrity of findings through cryptographic hashing, optimizing the digital forensic investigation process.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102517"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-02-02DOI: 10.1016/j.softx.2026.102535
Lijian Ren
Auto-PHSSCW ABAQUS is an open-source Python package that automates the parametric buckling-to-collapse modeling of H-shaped steel composite walls in Abaqus/CAE. Unlike standard macro-based scripts, this tool employs an integrated workflow combining geometric modeling, mesh generation, and multi-step solver orchestration. A key novel technical solution is the implementation of an anchor-based smart keyword injection algorithm, which dynamically manipulates the Abaqus keyword block to automate the injection of eigenmode-based geometric imperfections—a process traditionally requiring manual input files (.inp) editing to bypass standard API limitations. Furthermore, to ensure robustness during high-throughput parametric studies, the software utilizes coordinate-based bounding box algorithms for topological identification, eliminating mesh dependency errors common in index-based scripting. The workflow also features a closed-loop data transfer protocol that autonomously links linear eigenvalue results to non-linear Static-Riks collapse analysis. The tool supports continuous, separated, and bolted splice joints, and has successfully generated over 140,000 simulations to generate datasets for machine learning, significantly lowering the computational barrier for stability research.
{"title":"Auto-PHSSCW ABAQUS: An integrated, python-based workflow for automated buckling-to-collapse analysis of H-shaped steel composite walls","authors":"Lijian Ren","doi":"10.1016/j.softx.2026.102535","DOIUrl":"10.1016/j.softx.2026.102535","url":null,"abstract":"<div><div>Auto-PHSSCW ABAQUS is an open-source Python package that automates the parametric buckling-to-collapse modeling of H-shaped steel composite walls in Abaqus/CAE. Unlike standard macro-based scripts, this tool employs an integrated workflow combining geometric modeling, mesh generation, and multi-step solver orchestration. A key novel technical solution is the implementation of an anchor-based smart keyword injection algorithm, which dynamically manipulates the Abaqus keyword block to automate the injection of eigenmode-based geometric imperfections—a process traditionally requiring manual input files (.inp) editing to bypass standard API limitations. Furthermore, to ensure robustness during high-throughput parametric studies, the software utilizes coordinate-based bounding box algorithms for topological identification, eliminating mesh dependency errors common in index-based scripting. The workflow also features a closed-loop data transfer protocol that autonomously links linear eigenvalue results to non-linear Static-Riks collapse analysis. The tool supports continuous, separated, and bolted splice joints, and has successfully generated over 140,000 simulations to generate datasets for machine learning, significantly lowering the computational barrier for stability research.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102535"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-02-03DOI: 10.1016/j.softx.2026.102542
Diego Conde-Herreros , Oscar Corcho , David Chaves-Fraga
Knowledge Graphs are commonly organised according to the structure of existing ontologies, which define the concepts, relations, and restrictions of the domain of the KG. There are ontology-dependent assets that guide how data from heterogeneous sources is integrated, transformed, validated, and exploited in the KG, such as mapping rules and validation constraints. As ontologies evolve over time, these changes must be consistently reflected in the dependent assets, ensuring that the resulting KG remains aligned with the updated ontology. While ontology evolution has been widely studied, the propagation of changes to dependent artifacts remains an open challenge, requiring manual effort that makes the process slow, error-prone, and costly. In this paper, we present OntoRipple, a set of algorithms integrated into a tool that automates the propagation of ontology changes into RML mappings and SHACL shapes to construct and validate Knowledge Graphs, ensuring consistency with the evolving ontology in a fully declarative workflow.
{"title":"OntoRipple: Making waves in the knowledge graph lifecycle","authors":"Diego Conde-Herreros , Oscar Corcho , David Chaves-Fraga","doi":"10.1016/j.softx.2026.102542","DOIUrl":"10.1016/j.softx.2026.102542","url":null,"abstract":"<div><div>Knowledge Graphs are commonly organised according to the structure of existing ontologies, which define the concepts, relations, and restrictions of the domain of the KG. There are ontology-dependent assets that guide how data from heterogeneous sources is integrated, transformed, validated, and exploited in the KG, such as mapping rules and validation constraints. As ontologies evolve over time, these changes must be consistently reflected in the dependent assets, ensuring that the resulting KG remains aligned with the updated ontology. While ontology evolution has been widely studied, the propagation of changes to dependent artifacts remains an open challenge, requiring manual effort that makes the process slow, error-prone, and costly. In this paper, we present OntoRipple, a set of algorithms integrated into a tool that automates the propagation of ontology changes into RML mappings and SHACL shapes to construct and validate Knowledge Graphs, ensuring consistency with the evolving ontology in a fully declarative workflow.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102542"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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