The exponential growth of big data has elevated statistical analytics to a pivotal role in data science. While big data offers an extensive repository of information, it simultaneously poses unique challenges and presents remarkable opportunities.

Statistical analytics is essential for transforming raw data into actionable knowledge, extracting valuable insights, and facilitating evidence-based decision-making in the realm of big data. By employing systematic approaches, statistical analytics enables the analysis of vast datasets to uncover hidden patterns, trends, and correlations. In the big data era, the significance of statistical analytics is amplified, as traditional methods often fall short when managing complex, large-scale datasets. Hence, advancing statistical analytics is crucial for effectively leveraging big data across diverse fields such as finance, healthcare, marketing, and social sciences. Exploring cutting-edge theories and methods allows researchers and practitioners to deepen their understanding of statistical analytics while developing innovative solutions to specific big data challenges.

Despite the growing focus on big data and statistical analytics, challenges persist in applying statistical analytics to big data applications persist. These include training efficient large-scale AI models with minimal computational resources, leveraging advanced statistical analytics to optimize economic activities, and formalizing the relationship between big data and statistical analytics. This opens broad avenue for exploring the latest advancements in statistical analytics within the context of big data. We are excited to announce this special issue of Big Data Mining and Analytics, dedicated to sharing the latest advances, current challenges, and potential applications of Big Data Statistical Analytics (BDSA).

This special issue will cover a wide array of topics pertinent to Big Data Statistical Analytics, including, but not limited to:

• Statistical machine learning algorithms for big data analytics
• Distributed statistical computing and processing for big data
• Deep learning models and architectures for big data statistical analytics
• Novel statistical inference methods for large-scale dataset analysis
• Privacy-preserving statistical analytics for sensitive big data
• Real-time statistical methods and streaming data analytics for big data
• Statistical modeling and prediction for massive datasets
• Anomaly detection and outlier identification through statistical analytics in big data
• Scalable and efficient algorithms for statistical analytics on distributed big data platforms
• Nonparametric statistical methods tailored for big data
• Real-world applications of statistical analytics
• Other relevant statistical analytics theories, methods, and algorithms for big data

Authors are invited to submit their full research papers, aligning with the journal's general scope. Submissions will undergo peer review to ensure quality and relevance. Notification of acceptance will be issued following the review process.

Important dates:

• Expected first submission: 31 January 2025
• First review round completed: 31 March 2025
• Revised manuscripts due: 31 May 2025
• Completion of the review and revision process (final notification): 31 August 2025

Guest editors:

• Joshua Zhexue Huang, Ph.D., Professor, Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, China. E-mail : huang@szu.edu.cn
• Yulin He, Ph.D., Research Fellow, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), China. E-mail: yulinhe@gml.ac.cn
• Ponnuthurai Nagaratnam Suganthan, Ph.D., Professor, KINDI Center for Computing Research, College of Engineering, Qatar University, Qatar. E-mail : epnsugan@ntu.edu.sg