Imagen de Portada

Big Data Analytics in IoT, social media, NLP, and information security: trends, challenges, and applications

Guardado en:
Publicado en:Journal of Big Data vol. 12, no. 1 (Jun 2025), p. 150
Publicado:
Springer Nature B.V.
Materias:
CNN
Big Data
Social networks
Artificial neural networks
Taxonomy
Data analysis
Machine learning
Decision trees
Accuracy
Self-supervised learning
Performance measurement
Graph neural networks
Computational efficiency
Computing costs
Algorithms
Natural language processing
Unstructured data
Real time
Cybersecurity
Computing time
Digital media
Synthetic data
Experiments
Classification
Property
Robustness
Mass media
Security
Social media
Neural networks
Trees
Data processing
Suitability
Application
Decision making
Acceso en línea:Citation/Abstract
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:This paper presents a comprehensive, domain-specific survey and experimental evaluation of machine learning techniques for Big Data Analytics across four critical domains: IoT, Social Media, Natural Language Processing (NLP), and Information Security. A novel taxonomic framework is introduced to classify and analyze the suitability of algorithms based on empirical, experimental, and computational perspectives. The study integrates large-scale experimental benchmarking of key techniques—including CNN, XGBoost, Self-Supervised Learning (SSL), Graph Neural Networks (GNN), ELM, KNN, and Decision Trees—using real-world and synthetic datasets, and evaluates them across five performance metrics: accuracy, F1-score, precision, recall, and computational time. Key findings reveal that: (1) GNN and Self-Supervised Learning (SSL) are top performers in terms of predictive performance and efficiency in domains such as IoT and Social Media, (2) XGBoost and CNN offer superior accuracy and robustness across structured and unstructured data tasks, though CNN incurs higher computational costs, (3) ELM and Decision Trees are better suited for lightweight or interpretable applications, and (4) KNN generally underperforms in scalability and predictive strength for large-scale tasks. The taxonomy and experiments collectively demonstrate the need for context-aware algorithm selection, particularly for real-time and scalable Big Data applications. By aligning algorithmic properties with domain-specific challenges, this study offers actionable insights for researchers and practitioners seeking effective analytic strategies in the evolving landscape of Big Data.
ISSN:2196-1115
DOI:10.1186/s40537-025-01192-9
Fuente:ABI/INFORM Global