Aims and scope

The aim of Service-Oriented Computing & Applications is to publish original and high quality research results on the service-oriented computing (SOC) paradigm, models and technologies that have significant contributions to the advancement of service oriented systems and their applications.

Service-oriented computing has emerged as a major research topic in the past few years. Although the concept has evolved from earlier component-based software frameworks, web service standards are based on the readily and openly available internet protocols, and thus are easier and cheaper for companies to adopt. The strong support from major computer and IT service companies further speeds up the acceptance and adoption of SOC.

However, service developers and users face many significant challenges and opportunities that are introduced by the dynamism of software service environments and requirements. This requires new concepts, methods, models, and technologies along with flexible and adaptive infrastructure for services developments and management in order to facilitate the on-demand integration and formation of services across different platforms and organizations. The success of service-oriented systems relies on the adoption of the derived technologies to meet the demands from the evolving environment.

The journal focuses on the issues and research results related to the development of service-oriented technology, including service infrastructures, theoretical foundations and their applications and experiences in service-oriented computing. Topics covered by the journal include, but are not limited to, the following subjects as they relate to service-oriented computing and applications:

Foundations of Service-Oriented Computing:

    Service design
    Service discovery
    Microservice
    Service Software engineering
    Service deployment
    Service orchestration/composition
    Quality of Service

Intelligent Service Computing:

    Architectures and frameworks for intelligent service computing
    Integration of AI techniques
    Intelligent service discovery, composition, and orchestration
    NLP for service interoperation and interpretation
    Prompt engineering for services

Cloud Services and Big Data:

    Cloud, fog, edge computing
    Cloud service management
    Cloud sustainability and Green ICT (services)
    Computing continuum
    Data-driven service quality assessment and improvement
    Big data analytics for services

Everything as a Service:

    Software as a Service (SaaS)
    Platform as a Service (PaaS)
    Infrastructure as a Service (IaaS)
    Data as a Service
    IoT as a service
    Metaverse as a Service
    AI as a Service (Services for AI and AI for Services)
    Container as a Service

Services and Humans:

    Human-robot collaboration in service delivery and assistance
    Personalized and context-aware service provisioning
    Services for Human-AI interaction and collaboration
    Cognitive Services

Security, Privacy, and Trust:

    Security and privacy challenges in AI-driven service ecosystems
    Trustworthiness and reliability of intelligent service provisions
    Ethical intelligent services
    Blockchain, Explainable and transparent intelligent services

Service-Oriented Applications:

    Real-world applications of intelligent service computing in various domains (e.g., healthcare, finance, e-commerce, logistics, energy, transportation etc.)
    Quantum service architectures and experiences
    Robotics and service architectures for robotics, Smart Cities, Smart Buildings, Smart Grids
    Success stories and lessons learned from service deployments

Special Issue on Generative AI for Enterprise Service Systems: Industrial Intelligence, Management & Collaboration
Submission Date: 2026-03-31

The rapid evolution of digital service networks, driven by advancements in Artificial Intelligence (AI), has created both unprecedented opportunities and complex challenges. Modern enterprises - from cloud-native infrastructures and IoT-driven systems to emerging data ecosystems- demand efficient orchestration, robust security, and seamless collaboration to sustain a competitive edge. However, enabling dynamic and trustworthy collaboration not just within a single enterprise but across entire value chains strains traditional management and automation techniques.

The emergence of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs) and Foundation Models, offers transformative potential to overcome these barriers. By deeply integrating GenAI with service computing, we can create a new generation of intelligent enterprise services capable of understanding complex business requirements, autonomously planning and coordinating cross-organizational workflows, and interacting contextually with human experts. This fusion enables transformative applications, from service-oriented digital twins that optimize business processes to autonomous AI agents that negotiate contracts and manage supply chains.

This special issue invites high-quality, original contributions that advance the cutting-edge theories, innovative methodologies, core technologies, and practical solutions for building intelligent, collaborative, and autonomous enterprise ecosystems through the synergy of GenAI and service computing. We seek both foundational research and real-world case studies that demonstrate how this combination can redefine the architecture, engineering, and application of next-generation enterprise systems.

Contributions are invited on, but not restricted to, the following themes:

The special issue seeks to address a broad range of topics related to the integration of LLMs in the digital service network lifecycle. Topics of interest include, but are not limited to:

1. Core Architectures & Models for Enterprise AI Services

• Leverage LLMs for designing event-driven and service-oriented hybrid architectures for intelligent enterprises.
• Integrate LLM-driven reinforcement learning for the adaptive service generation and orchestration across the enterprise edge-to-cloud continuum.
• Develop "Enterprise-Model-as-a-Service" (EMaaS) architectures to encapsulate simulation, prediction, and business optimization.
• Design autonomous enterprise systems for cross-organizational collaboration using microservices and AI Agent models.
• Develop federated learning architectures for privacy-preserving cross-enterprise GenAI services

2. AI-Empowered Engineering & Automation of Enterprise Services

• Utilize LLMs to automate the generation of business processes (e.g., BPMN) and executable workflows from natural language specifications in enterprise contexts.
• Implement autonomous service discovery, composition, and dynamic reconfiguration driven by LLMbased planning for enterprise value chains.
• Employ LLMs for anomaly detection and root-cause analysis in enterprise-critical services testing and monitoring.
• Apply MLOps and DevOps to manage continuous lifecycle of enterprise AI services.
• Implement self-adaptive service recovery through LLM-generated remediation plans

3. Key Enterprise Application Systems & Human-Centric Services

• Adopt LLM-enhanced Digital Twins for business process optimization, supply chain simulation, and decision-making.
• Enable LLM-based agents for supply chain negotiation, resource management, and project scheduling.
• Combine human expertise with AI for decision-making through human-in-the-loop frameworks.
• Innovate business process automation by encapsulating tasks as AI-orchestrated services.

4. Trustworthy Enterprise Service Ecosystems & Governance

• Create LLM-based architectures and governance models for Trusted Enterprise Data Spaces, ensuring Data-as-a-Service (DaaS) and data sovereignty.
• Explore "Enterprise Regulation Agent" approaches, employing LLMs for automated compliance checking, adaptive monitoring, and risk assessment in business operations.
• Enhance trust, privacy, and security with LLM-guided threat modeling and multi-party computation
• Implement formal verification and uncertainty quantification for LLM-driven service decisions.

5. Large-Scale Enterprise Demonstrations & Application Scenarios

• Showcase enterprise deployments in sectors such as finance, healthcare, logistics, smart government using LLM-enhanced service architectures.
• Establish frameworks and best practices for integrating LLMs in large-scale enterprise deployments.
• Evaluate and validate LLM-driven enterprise service networks for performance, reliability, and business impact.
• Analyze carbon footprint of GenAI service deployments.