The Advances and Applications of Smart Computing and Big Data Analysis

This Special Issue emphasizes big data analytics and machine learning as enabling foundations for smart computing in data-intensive, operational settings. The focus is on methods and systems that convert large-scale, high-velocity, and heterogeneous data into reliable predictions, actionable insights, and decision support, while accounting for practical constraints such as latency, resource limits, privacy, and data quality variation. We particularly welcome contributions that advance scalable ML (distributed training, online/stream learning, efficient inference), robust modeling (handling missingness, drift, imbalance, noisy labels), and reproducible evaluation (well-defined baselines, ablation studies, realistic workloads). Submissions should clearly articulate what is new and why it matters, and they should validate claims using representative datasets, deployments, or cross-domain benchmarks. In addition to core smart computing themes (edge–cloud orchestration, IoT and cyber-physical systems, MLOps, governance), the Special Issue explicitly invites application-driven research and case studies in sectors where big data and ML are rapidly reshaping practice, including the economy and finance, digital marketing, tourism, education, healthcare, smart cities, manufacturing, transportation, and cybersecurity.

Dear Colleagues,

Big data and machine learning are now central to smart computing, not only because of the volume and velocity of modern data, but also because many operational decisions increasingly depend on predictive and adaptive models. Organizations face heterogeneous data sources (transactions, sensors, mobile traces, platform logs, text and images), continuous streaming inputs, and rapid feedback loops that require models to be trained, deployed, monitored, and updated under real-world constraints. As a result, progress in this area depends on both methodological advances in machine learning and practical advances in data engineering and computing systems. Achieving reliable outcomes requires attention to scalability, robustness to drift and bias, privacy and security, and reproducible evaluation rather than isolated accuracy improvements.

We are pleased to invite you to submit your latest research to the Special Issue “The Advances and Applications of Smart Computing and Big Data Analysis”.

This Special Issue aims to showcase high-quality research that advances big data analytics and machine learning methods, architectures, and applications for smart computing. We welcome contributions spanning algorithmic innovation (e.g., efficient learning, trustworthy ML, automated feature learning), systems and platform design (distributed processing, edge–cloud orchestration, accelerators, MLOps), and domain-focused studies that demonstrate measurable impact and transferable insights.

Suggested themes and article types for submissions:

• Big data analytics and engineering: data integration, lakehouse architectures, stream processing, feature engineering at scale, metadata/provenance, data quality management
• Scalable machine learning: distributed training, parallel inference, online and incremental learning, model compression, resource-aware ML
• Trustworthy and responsible ML: privacy-preserving analytics, federated learning, secure computation, robustness, fairness, auditing, compliance-aware model governance
• Smart computing infrastructures: edge–cloud collaboration, IoT/cyber-physical systems, scheduling and optimization for ML pipelines, hardware acceleration
• Application sectors (explicitly in scope): economy and finance, digital marketing, tourism, education, healthcare, smart cities, manufacturing, transportation, cybersecurity

Article types: original research articles; systematic reviews or surveys (with transparent methodology); empirical benchmarking studies; applied case studies with rigorous validation and clear technical contribution

We look forward to receiving your contributions.

Dr. Leonidas Theodorakopoulos
Dr. Konstantinos Gkillas
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. 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.

• big data analytics
• machine learning
• scalable learning and inference
• distributed computing
• streaming analytics
• federated learning
• edge–cloud computing
• privacy-preserving analytics
• applications in digital marketing, tourism and education