Guest Editor(s)
Prof. Dr. Sebastián Ventura
Email: sventura@uco.es
Affiliation: Department of Computing and Artificial Intelligence, University of Córdoba, Córdoba, Spain
Homepage:
Research Interests: data-based, artificial intelligence, machine learning, data mining
Prof. Dr. Eduardo Pérez
Email: eperez15@us.es
Affiliation: Department of Computer Science and Artificial Intelligence, University of Seville, Seville, Spain
Homepage:
Research Interests: machine learning, automatic lesion diagnosis, AI in healthcare
Summary
The volume, velocity, heterogeneity, and evolving nature of modern data are challenging conventional analytics. Turning these data into actionable knowledge requires methods that scale across distributed infrastructures, learn continuously from streams, and remain interpretable under uncertainty. Soft computing offers a natural foundation through fuzzy systems, evolutionary computation, neural models, and hybrid learning strategies, yet their deployment in Big Data environments still raises major questions about efficiency, adaptation, data quality, imbalance, and transparency.
This Special Issue seeks original research and authoritative reviews on scalable, adaptive, and interpretable soft computing for Big Data analytics. It will connect algorithmic innovation with distributed and streaming platforms, emphasizing reproducible solutions validated on large, heterogeneous, or high-velocity datasets.
Topics include distributed and federated learning; Spark-, Flink-, cloud-, edge-, and HPC-based analytics; data preprocessing, reduction, integration, quality, and imbalance; stream mining, online learning, concept-drift detection, and real-time decision support; fuzzy, evolutionary, ensemble, deep, and hybrid models; interpretable pattern and subgroup discovery; scalable optimization and AutoML; privacy-aware and robust analytics; and applications in healthcare, smart cities, industry, education, finance, and environmental science.
By uniting data engineering and intelligent analytics, this collection aims to advance Big Data systems that are efficient, adaptive, understandable, dependable, responsible, and useful in practice.
Keywords
big data analytics, soft computing, distributed learning, data stream mining, interpretable machine learning, evolutionary computation, fuzzy systems, scalable data preprocessing, concept drift, real-time analytics
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