Special Issues
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Big Data-Driven Intelligent Decision Systems

Submission Deadline: 31 January 2026 View: 568 Submit to Special Issue

Guest Editors

Prof. Jaewon Choi

Email: jaewonchoi@sch.ac.kr

Affiliation: Department of Business Administration, Soonchunhyang University, Asan, 31538, South Korea

Homepage:

Research Interests: big data analytics, digital marketing, consumer behavior, social networks, intelligent agent


Summary

In the era of digital transformation, the exponential growth of big data has fundamentally reshaped the decision-making landscape for organizations, governments, and industries. By harnessing intelligent algorithms—such as machine learning, deep learning, and optimization techniques—decision systems have become increasingly accurate, real-time, and adaptive across a wide array of application domains.
This special issue seeks to showcase state-of-the-art research and practical advancements in intelligent decision systems driven by big data. We invite original submissions that present novel methodologies, architectural frameworks, algorithmic innovations, and real-world applications. Relevant topics include, but are not limited to, data-driven decision-making models, intelligent analytics frameworks, real-time decision engines, and domain-specific implementations in areas such as healthcare, finance, manufacturing, smart cities, and logistics.

Topics of interest include, but are not limited to:
- Big data analytics for decision support
- Machine learning-based decision systems
- Real-time and streaming decision-making algorithms
- Data-driven optimization and predictive modeling
- Intelligent systems in industrial and enterprise settings
- Decision-making in smart cities and IoT environments


Keywords

Big Data Analytics, Intelligent Decision Systems, Machine Learning, Real-Time Decision-Making, Predictive Modeling, Optimization, Smart Applications

Published Papers


  • Open Access

    ARTICLE

    Spatio-Temporal Earthquake Analysis via Data Warehousing for Big Data-Driven Decision Systems

    Georgia Garani, George Pramantiotis, Francisco Javier Moreno Arboleda
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071509
    (This article belongs to the Special Issue: Big Data-Driven Intelligent Decision Systems)
    Abstract Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation. Modern seismological research produces vast volumes of heterogeneous data from seismic networks, satellite observations, and geospatial repositories, creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making. Data warehousing technologies provide a robust foundation for this purpose; however, existing earthquake-oriented data warehouses remain limited, often relying on simplified schemas, domain-specific analytics, or cataloguing efforts. This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity. The framework integrates… More >

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