Machine Learning Applications for Big Data Analysis

Dear Colleagues,

The rapid expansion of big data across domains such as healthcare, social sciences, cybersecurity, and smart systems has created unprecedented opportunities and challenges for data-driven discovery. Machine learning has emerged as a central methodology for extracting meaningful patterns, predicting complex behaviors, and supporting intelligent decision making from large-scale, high-dimensional, and heterogeneous datasets. This Special Issue focuses on advancing theoretical, methodological, and applied research on machine learning techniques tailored for big data environments. While the existing literature has explored ML algorithms and big data analytics separately, there remains a critical need for integrative research that addresses scalability, interpretability, data quality, and ethical implications in real-world big data contexts. By bringing together interdisciplinary perspectives, this issue aims to bridge gaps between computational innovation and practical applications. This Special Issue aligns closely with the scope of BDCC as it emphasizes cognitive computing, intelligent systems, and advanced analytics. It will provide a platform for cutting-edge research that enhances the efficiency, reliability, and societal impact of machine learning-driven big data analysis across diverse scientific and applied fields.

1. Introduction, including scientific background and highlighting the importance of this research area.

The exponential growth of digital data generated from social media, sensors, healthcare systems, financial transactions, and public safety infrastructures has fundamentally transformed how knowledge is produced and utilized. Big data is characterized by its volume, velocity, variety, and veracity, requiring advanced computational techniques capable of processing and interpreting complex datasets. Machine learning has become a cornerstone of big data analytics, enabling automated pattern recognition, predictive modeling, anomaly detection, and intelligent decision support. Recent advancements in deep learning, natural language processing, and AI-driven analytics have significantly improved our ability to analyze large-scale structured and unstructured data. However, major challenges remain, including data heterogeneity, scalability, model interpretability, algorithmic bias, and ethical considerations. Addressing these challenges is essential for developing robust and trustworthy machine learning systems that can operate effectively in big data environments. Therefore, continued interdisciplinary research at the intersection of machine learning and big data is both timely and critically important for scientific innovation and real-world problem solving.

2. Aim of the Special Issue and how the subject relates to the journal scope.

This Special Issue aims to explore innovative machine learning methodologies, frameworks, and applications designed specifically for big data analysis. It seeks to advance both theoretical developments and applied research that enhance the efficiency, scalability, interpretability, and cognitive intelligence of data-driven systems. The topic strongly aligns with the scope of Big Data and Cognitive Computing (BDCC), which emphasizes intelligent data processing, cognitive analytics, and computational intelligence. By focusing on machine learning applications in big data contexts, this issue contributes directly to the journal’s mission of promoting interdisciplinary research that integrates artificial intelligence, cognitive computing, and large-scale data analytics. This Special Issue will also highlight emerging trends such as explainable AI, AI ethics, and domain-specific big data solutions, thereby fostering scholarly dialog and practical innovation.

3. Suggest themes.

Original research articles and reviews are welcome in this Special Issue. Research areas may include (but are not limited to) the following:

• Machine learning analysis;
• Explainable and interpretable AI in big data environments;
• Machine learning for social and behavioral data analysis;
• Big data applications in healthcare, criminology, finance, and smart cities;
• Natural language processing and text mining with big data;
• Data mining, pattern recognition, and predictive analytics;
• Cloud and distributed machine learning systems;
• Ethical, privacy, and fairness issues in big data machine learning;
• Data quality, preprocessing, and feature engineering for large datasets;
• Real-time and streaming data analytics;
• AI-driven decision-support systems;
• Hybrid AI models integrating cognitive computing and big data.

We look forward to receiving high-quality submissions that advance methodological innovation and interdisciplinary applications in machine learning and big data analysis.

Dr. Juyoung Song
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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.

• machine learning
• big data
• artificial intelligence
• data mining
• data analysis