Application of Big Data Technology Based on Machine Learning
A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 November 2026 | Viewed by 991
Editor
Interests: data compression; machine learning and AI; signal and image processing; data analytics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid growth of data across various sectors has led to unprecedented challenges and opportunities in data analysis and decision-making in this data-driven AI era. Big Data technologies, when integrated with the predictive and adaptive capabilities of Machine Learning (ML), offer powerful tools to extract meaningful insights from massive, complex, and high-dimensional datasets. These technologies have significantly impacted fields such as healthcare, finance, transportation, cybersecurity, and smart cities, among others.
Machine Learning enhances Big Data analytics by providing intelligent mechanisms to model non-linear patterns, perform real-time data processing, and improve the scalability of predictive systems. Conversely, Big Data infrastructures (such as Hadoop, Spark, and cloud computing platforms) enable ML algorithms to efficiently operate on large-scale datasets. This symbiotic relationship represents a transformative shift in how data is processed, interpreted, and utilized for decision support, innovation, and automation.
This Special Issue aims to highlight cutting-edge research, recent developments, and practical implementations at the intersection of Big Data and Machine Learning. It will allow researchers, practitioners, and industry experts to share advances, challenges, and future directions in this vibrant and rapidly evolving domain.
We welcome original research articles and reviews covering areas including (but not limited to) the following:
- Scalable Machine Learning algorithms for Big Data analytics;
- Real-time data processing and stream analytics;
- Applications in healthcare, finance, and smart cities;
- Cloud and edge computing for distributed Machine Learning;
- Security and privacy issues in Big Data and ML integration;
- Deep learning frameworks optimized for large-scale data;
- Data preprocessing, feature selection, and dimensionality reduction techniques;
- Visualization and interpretability of ML-driven Big Data insights.
We look forward to receiving your contributions.
Dr. W. David Pan
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- big data
- machine learning
- deep learning
- data analytics
- scalable algorithms
- cloud computing
- data mining
- real-time analytics
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