Machine Learning for Large-Scale Data Processing: Algorithms and Applications

In the current era of big data, the volume of information continues to grow at an unprecedented rate, giving rise to the crucial need for efficient large-scale data processing. Machine learning has emerged as a powerful tool to handle this challenge, providing ways to extract valuable insights and knowledge from massive datasets. However, traditional machine learning algorithms often face limitations when dealing with large-scale data due to issues like computational complexity, memory constraints, and scalability. Consequently, there is a pressing demand for developing advanced machine learning algorithms specifically designed for large-scale data processing, along with exploring their diverse applications across different domains.


This Research Topic aims to bring together researchers and practitioners from both academia and industry to share their latest findings and innovative ideas in the field of machine learning for large-scale data processing. We encourage submissions that present novel algorithms, methodologies, and techniques that address the challenges of scalability, efficiency, and accuracy in machine learning models when applied to large-scale data. Furthermore, we are interested in exploring the practical applications of these algorithms in various real-world scenarios, such as in the fields of healthcare, finance, telecommunications, and social media, among others. By fostering collaboration and exchange of ideas, we hope to advance the development of machine learning algorithms for large-scale data processing and their applications in solving complex real-world problems.


The scope of this Research Topic includes, but is not limited to, the following research areas and technologies:

(1) Machine Learning Algorithms:

a. Theoretical foundations and algorithm design

b. Optimization techniques for scalability and efficiency

c. Robust and scalable learning algorithms

(2) Large-Scale Data Processing:

a. Efficient data preprocessing and feature engineering

b. Data integration and fusion techniques

(3) Innovative Frameworks:

a. Novel parallel and distributed computing frameworks

b. Energy-efficient machine learning frameworks

(4) Applications and Case Studies:

a. Applications in image recognition, resource scheduling, power systems, unmanned control systems, swarm intelligence

b. Use cases in healthcare safety, traffic control, smart cities, financial forecasting, environmental monitoring


We encourage the submission of various types of manuscripts, including original research articles, review papers, technical notes, and perspective pieces, provided they offer significant insights and advancements in the field of machine learning for large-scale data processing.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Large-scale data processing, artificial intelligence, machine learning, theories and applications, distributed systems, parallel computing, data mining, pattern recognition, real-time data analytics, optimization algorithms

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.