Machine-Learning-Enabled Big Data Analysis: Advancements, Applications and Challenges

Dear Colleagues,

Due to the emergence of technology and the excessive use of data-generating devices, we have observed an exponential increase in the volume and velocity of data generation. The use of big data analytics has become pivotal in extracting valuable information (including market insights, hidden patterns, unknown correlations, and customer preferences) in various application domains. Recent advancements in data-driven deep learning and machine learning have paved the way for the analysis of large-scale datasets in diverse domains, such as healthcare, weather, transportation, manufacturing, energy, social media, and agriculture. Moreover, sophisticated techniques and analytical tools are being utilized to investigate big data, enabling researchers to visualize novel meanings and interpretations of the data, which can assist in data exploration and simplify complex analytics processes associated with big data. By collating cutting-edge research in this interdisciplinary field, this Special Issue intends to advance state-of-the-art techniques, methodologies, and applications for analyzing and leveraging big data using machine learning algorithms.

In light of these developments, we cordially invite the academic community and relevant industry partners to submit original research articles and reviews to this Special Issue, with a specific focus on the following themes:

• Scalable machine learning algorithms for big data analysis;
• Deep learning architectures for processing and analyzing big data;
• Feature selection and dimensionality reduction techniques for big data analysis;
• Ensemble learning approaches for big data analysis;
• Big-data-enabled edge intelligence;
• Transfer learning methods for leveraging big data across domains;
• Privacy-preserving machine learning techniques for big data analysis;
• Explainable and interpretable machine learning models for big data analysis;
• Online and streaming algorithms for the real-time analysis of big data;
• Machine-learning-enabled video/VR data compression and analysis;
• Machine-learning-enabled analysis techniques for IoT data;
• Advanced tools and techniques for big data visualization;
• Federated learning approaches for distributed big data analysis;
• Reinforcement learning algorithms for optimizing big data analysis tasks;
• Time series analysis and forecasting using machine learning on big data;
• Graph-based machine learning methods for analyzing large-scale networks;
• Anomaly detection and outlier analysis in big data using machine learning;
• Unsupervised learning techniques for clustering and discovering patterns in big data;
• Machine learning for automated data preprocessing and cleaning in big data analysis.

We look forward to receiving your contributions.

Dr. Mubeen Ghafoor
Guest Editor

Manuscript Submission Information

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• data analysis
• machine learning
• deep learning
• big data
• data science
• data streams
• video/VR data compression
• data mining