Guest Editors
Prof. Andreas Kanavos
Email: akanavos@ionio.gr
Affiliation: Department of Informatics, Ionian University, Corfu, Greece
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Research Interests: big data analytics, machine learning and deep learning, scalable AI systems, distributed data processingarchitectures, predictive modelling, data-driven decision support systems, intelligent information systems, explainable and trustworthy AI
Dr Alaa Mohasseb
Email: alaa.mohasseb@port.ac.uk
Affiliation: School of Computing, University of Portsmouth, United Kingdom
Homepage: Alaa Mohasseb - University of Portsmouth
Research Interests: artificial intelligence (AI), with a particular emphasis on natural language processing (NLP) and machine learning
Summary
The rapid growth of large-scale heterogeneous data generated by digital platforms, IoT ecosystems, enterprise systems, and public infrastructures has intensified the need for advanced big data analytics and intelligent computational frameworks. Modern applications increasingly demand scalable, efficient, and trustworthy AI systems capable of processing high-volume, high-velocity, and high-variety data streams.
This Special Issue aims to explore novel methodologies, architectures, and applied solutions in big data analytics, machine learning, and intelligent systems. Emphasis will be placed on scalable data processing pipelines, predictive modelling, deep learning on large-scale datasets, explainable AI in data-intensive environments, and data-driven decision support systems.
Contributions addressing both theoretical advances and real-world deployments are welcome, particularly in domains such as public sector digital transformation, smart infrastructures, cyber-physical systems, and enterprise intelligence. The Special Issue seeks to bridge foundational research with practical implementation challenges in modern big data ecosystems.
This Special Issue focuses on advanced algorithms, architectures, and applied frameworks for large-scale data processing and intelligent analytics. Topics include machine learning on big data platforms, scalable AI systems, distributed data processing, explainable AI, performance optimisation, and real-world data-driven applications. Both methodological research and industrial case studies are encouraged.
· Scalable Machine Learning for Big Data
· Distributed and Cloud-Based Data Processing
· Deep Learning on Large-Scale Datasets
· Big Data in Public Sector and Smart Infrastructure
· Explainable and Trustworthy AI in Data-Intensive Systems
· Predictive Analytics and Decision Support Systems
· Data Engineering and High-Performance Computing
Keywords
big data analytics, machine learning, distributed systems, scalable AI, deep learning, data engineering, predictive modelling, intelligent systems
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