Advanced Software and Machine Learning Techniques for System Architectures and Big Data

Dear Colleagues,

In recent years, the explosive growth of data and the increasing complexity of computing systems have posed new challenges to traditional system architectures. At the same time, significant advances in software engineering and machine learning have opened up new opportunities for building intelligent, adaptive, and efficient systems. Leveraging artificial intelligence to enhance system design, monitoring, optimization, and scalability has become a critical research direction, especially in the context of big data and distributed environments. This rapidly evolving interdisciplinary field plays a pivotal role in the development of next-generation computing infrastructures, from cloud and edge computing to autonomous systems and intelligent analytics platforms.

This Special Issue aims to bring together original research and comprehensive reviews that explore the convergence of advanced software methodologies and machine learning techniques in the context of system architectures and big data. The Issue emphasizes system-level innovation—how software and AI/ML can enhance architectural design, automate resource management, optimize performance, and support large-scale deployment, especially in big data and distributed settings. Contributions that bridge practical system design with intelligent algorithmic techniques—providing solutions that are not only theoretically novel but also applicable to real-world computing infrastructures such as cloud, edge, and hybrid environments—are especially encouraged. 

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

• Machine learning techniques for optimizing system architecture design;
• Advanced software engineering methods in big data environments;
• Intelligent data processing and analytics frameworks;
• Automated and adaptive resource management in distributed systems;
• Integration of AI/ML with cloud and edge computing infrastructures;
• Security, privacy, and reliability in intelligent system architectures;
• Real-world applications of smart system designs in industry and society;
• Hybrid models combining forecasting, anomaly detection, and automated response mechanisms within intelligent architectures;
• Time series analysis, modeling, and forecasting in dynamic environments;
• Machine learning approaches for anomaly detection in system logs, network traffic, or operational metrics.

We look forward to receiving your contributions. 

Dr. Yan Qiao
Dr. Francesco Cauteruccio
Guest Editors

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.