Modeling, Simulation, and Automated Evolution in Complex Information Systems Engineering

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 239

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Guest Editor
Department of Informatics and Computer Engineering, Egaleo Park Campus, University of West Attica, 122 43 Athens, Greece
Interests: information systems; systems engineering; modeling; simulation

Special Issue Information

Dear Colleagues,

Modern Information Systems (IS) face unprecedented challenges related to scale, dynamism, and the need to continuously adhere to critical constraints (cost, compliance, energy). This Special Issue will explore how modeling, simulation, and advanced engineering practices—including intent-driven and multi-ops methodologies—can provide the necessary discipline for evolving these systems. We seek papers detailing innovative approaches to embedding non-functional requirements into system logic, utilizing DSLs and annotations for automation, and creating self-optimizing and auto-evolutionary architectures. Contributions on intelligent orchestration, continuous auditing, and practical deployments in complex, real-world sectors are particularly welcome, with the goal of creating more resilient, efficient, and governable future Information Systems.

We invite submissions focusing on, but not limited to, the following high-impact topics:

  • System Modeling for Continuous Evolution: Architectures and models that support auto-evolutionary capabilities, allowing for IS components to autonomously select, integrate, and adapt based on runtime metrics and changing requirements.
  • Performance and Quality-of-Service (QoS) Simulation: Techniques for predicting, simulating, and validating non-functional requirements (NFRs) such as performance, cost-efficiency (CostOps), and energy consumption (GreenOps) in distributed and hybrid IS environments.
  • Requirements-Driven Engineering: Methods utilizing high-level constraints (Intent-Driven Development) or Domain-Specific Languages (DSLs) and annotations to embed operational requirements and governance logic directly into component code and deployment specifications.
  • Automated Validation and Auditing: Frameworks and tools for continuous, automated auditing and testing against formal system requirements, supporting the proactive detection and mitigation of issues (e.g., security risks, performance bottlenecks) in operational environments.
  • Next-Generation DevOps and Orchestration: Intelligent orchestration (AIOps) approaches and parameterized workflow blueprints for automating CI/CD pipelines, resource provisioning, and dynamic runtime placement of IS components across the cloud/edge continuum.
  • Human-in-the-Loop Management: Modeling human–IS interactions through conversational agents or intelligent assistants to simplify complex operational tasks, accelerate developer productivity, and provide real-time context-aware management guidance.
  • Case Studies in Complex IS Domains: Practical applications demonstrating the modeling, simulation, and automated deployment of complex IS solutions in fields such as Smart Cities, Industrial IoT, and large-scale enterprise data management.

Dr. Anargyros Tsadimas
Guest Editor

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Keywords

  • information systems engineering
  • software evolution
  • DevOps
  • modeling and simulation
  • automated orchestration
  • system dynamics
  • continuous integration/deployment

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Published Papers (1 paper)

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Research

39 pages, 6514 KB  
Article
Accessibility Aware Employability Analytics Using Workplace Simulation Logic and Person Job Fit Modeling
by Mónica Rodas, Fernando Pesántez, Daniel Naranjo and Esteban Inga
Information 2026, 17(7), 662; https://doi.org/10.3390/info17070662 (registering DOI) - 8 Jul 2026
Abstract
The transition from education to employment remains a major challenge, particularly for individuals who may require accessibility support during competency assessment and occupational guidance. However, many current approaches remain fragmented because they evaluate soft skills, accessibility conditions, and occupational requirements as separate dimensions. [...] Read more.
The transition from education to employment remains a major challenge, particularly for individuals who may require accessibility support during competency assessment and occupational guidance. However, many current approaches remain fragmented because they evaluate soft skills, accessibility conditions, and occupational requirements as separate dimensions. This study presents an accessibility-aware computational proof of concept for employability analytics using workplace simulation logic, derived competency indicators, semantic modeling, clustering, person–job fit estimation, and heuristic multi-objective optimization. The framework integrates open secondary employability data, O*NET-derived occupational descriptors, and simulated accessibility scenarios within a reproducible analytical pipeline. The results show differentiated computational employability profiles, with mean person–job fit values of 0.85, 0.74, and 0.63 for high, medium, and low profiles, respectively. The derived competency indicators showed high internal consistency (α=0.905), although they are interpreted as exploratory proxy dimensions rather than as an exploratory psychometric scale. Principal component analysis indicated a dominant general employability factor, with the first component explaining 75.3% of the variance. The optimization layer produced interpretable heuristic convergence patterns and modeled scenario assignments under predefined validity, accessibility, alignment, and diagnostic criteria. Person–job fit was interpreted under sensitivity scenarios involving alternative competency weights, scalarization parameters, and accessibility assumptions. The study does not include observed participants with disabilities, measured accessibility support use, field simulator interaction records, or longitudinal employment outcomes. Therefore, the term accessibility-aware refers to the computational framework’s design orientation. At the same time, the empirical evidence should be interpreted as a secondary-data-based proof of concept rather than as validation of an inclusive simulator for future users with accessibility needs. The main numerical indicators were: high-profile mean fit = 0.85, medium-profile mean fit = 0.74, low-profile mean fit = 0.63, Cronbach’s alpha = 0.905, first principal component variance = 75.3%, and heuristic iterations = 900. Full article
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