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Open Access

ARTICLE

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

Georgia Garani1,*, George Pramantiotis2, Francisco Javier Moreno Arboleda3
1 Department of Digital Systems, School of Technology, Gaiopolis, University of Thessaly, Larisa, 41500, Greece
2 School of Science and Technology, Hellenic Open University, Patras, 26335, Greece
3 Departamento de Ciencias de la Computación y de la Decisión, Universidad Nacional de Colombia Sede Medellín, Medellín, 050021, Colombia
* Corresponding Author: Georgia Garani. Email: email
(This article belongs to the Special Issue: Big Data-Driven Intelligent Decision Systems)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.071509

Received 06 August 2025; Accepted 12 November 2025; Published online 02 December 2025

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 spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables. A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance, while the bridge-table schema remains advantageous for dimension-centric queries. To reconcile these trade-offs, a hybrid schema is proposed that retains both representations, ensuring balanced efficiency across heterogeneous workloads. The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity, improve query performance, and support multidimensional visualization. In doing so, it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience, risk mitigation, and emergency management.

Keywords

Data warehouse; data analysis; big data; decision systems; seismology; data visualization

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