Future Generation Computer Systems-The International Journal of eScience

Scope & Guideline

Shaping Tomorrow's Technology Today

Introduction

Explore the comprehensive scope of Future Generation Computer Systems-The International Journal of eScience through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore Future Generation Computer Systems-The International Journal of eScience in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN0167-739x
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1984 to 2025
AbbreviationFUTURE GENER COMP SY / Futur. Gener. Comp. Syst.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

Future Generation Computer Systems - The International Journal of eScience aims to explore and publish cutting-edge research in the field of computing systems, with a particular focus on the integration of emerging technologies like cloud computing, edge computing, artificial intelligence, and the Internet of Things (IoT).
  1. Cloud and Edge Computing Solutions:
    The journal emphasizes research on cloud computing architectures, services, and applications, along with innovative approaches to edge computing that enhance data processing and resource management.
  2. Federated Learning and Privacy-Preserving Techniques:
    There is a significant focus on federated learning, particularly in maintaining privacy and security in distributed environments, highlighting advancements in algorithms and frameworks that facilitate collaborative learning across devices.
  3. Big Data and Analytics:
    Research on big data management, analysis, and real-time processing is prevalent, with contributions that address challenges in data storage, retrieval, and analysis using advanced computational techniques.
  4. Cybersecurity and Trustworthy Systems:
    The journal publishes papers related to cybersecurity, particularly in the context of IoT and cloud systems, exploring methods for securing data, detecting anomalies, and ensuring trust in distributed environments.
  5. Artificial Intelligence and Machine Learning Applications:
    The integration of AI and ML in various applications, including healthcare, smart cities, and industrial IoT, is a core area of focus, showcasing innovative algorithms and frameworks for intelligent decision-making.
  6. Blockchain Technology and Its Applications:
    Blockchain technology is explored for its potential to enhance security, transparency, and efficiency in various applications, particularly in data sharing and integrity verification.
  7. Digital Twin and IoT Integration:
    The journal addresses the concept of digital twins, particularly how they can be leveraged for IoT applications in areas like smart cities, healthcare, and industry, emphasizing the importance of real-time data monitoring and analysis.
The journal is witnessing a rise in interest in several emerging themes, reflecting the evolving landscape of computing systems and the increasing complexity of challenges faced by researchers and practitioners.
  1. Quantum Computing and Algorithms:
    There is a growing trend towards exploring quantum computing, including the development of algorithms and applications that leverage quantum mechanics to solve complex problems faster than classical computing.
  2. Sustainable Computing Practices:
    Research focusing on energy-efficient computing and sustainable practices is gaining traction, reflecting a broader awareness of environmental impacts and the need for green technology solutions.
  3. AI-Driven Decision Support Systems:
    The integration of AI in decision support systems is increasingly being explored, particularly in healthcare and smart city applications, where intelligent systems can offer real-time insights and predictions.
  4. Integration of Multi-Agent Systems:
    The use of multi-agent systems for collaborative problem-solving in dynamic environments is emerging, with applications in logistics, transportation, and smart infrastructure.
  5. Resilient and Adaptive Systems:
    There is a notable increase in research focused on building resilient and adaptive systems that can handle disruptions and uncertainties, especially in critical infrastructure and industrial applications.
  6. Privacy-Preserving Machine Learning:
    The trend of privacy-preserving techniques in machine learning is on the rise, addressing concerns regarding data privacy and security in distributed learning settings.

Declining or Waning

While Future Generation Computer Systems continues to expand in many areas, certain themes have shown a decline in prominence, reflecting shifts in research interests and technological advancements.
  1. Traditional Data Management Systems:
    Research focused on traditional relational database management systems appears to be waning, as attention shifts towards more scalable, distributed, and cloud-based solutions that can handle big data and real-time analytics.
  2. Static Security Approaches:
    There is a noticeable decrease in papers discussing static security measures, with a shift towards more dynamic, adaptive security frameworks that respond to evolving threats in real-time.
  3. Legacy System Integrations:
    The integration of legacy systems with modern computing architectures is less frequently addressed, as organizations increasingly migrate to cloud-native solutions that do not rely on older technologies.
  4. Solely Hardware-Centric Solutions:
    Research that emphasizes purely hardware-centric solutions without considering software integration and optimization is becoming less relevant, as there is a growing trend towards holistic approaches that combine both hardware and software innovations.

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