Big Data Technology and Its Applications

Dear Colleagues,

The exponential growth of data across industries has intensified the demand for advanced techniques to extract actionable insights efficiently. Artificial intelligence (AI) has emerged as a transformative force, enabling intelligent data processing and mining through innovations such as deep learning, reinforcement learning, and federated learning. These technologies hold immense potential to revolutionize how organizations analyze, interpret, and leverage data for decision-making, predictive modeling, and automation. However, challenges such as scalability, real-time processing, ethical concerns, and integration with legacy systems remain critical barriers. This Special Issue seeks to explore cutting-edge advancements in AI-driven data processing and mining, highlighting their applications, methodologies, and ethical implications.

Primary objectives of this Special Issue:

1. Highlighting innovations in AI-driven data processing: Showcase novel algorithms, architectures, and frameworks that enhance data mining efficiency, accuracy, and adaptability compared to traditional methods.
2. Exploring cross-domain applications: Demonstrate how AI-powered data processing transforms sectors such as healthcare, finance, smart cities, and manufacturing, with a focus on real-world case studies.
3. Addressing integration and ethical challenges: Discuss strategies to overcome technical hurdles (e.g., data heterogeneity, computational complexity) and ethical issues (e.g., bias, privacy) in deploying AI-driven systems.

We welcome high-quality submissions that explore topics including, but not limited to, the following:

1. Theoretical advances:
• Novel AI architectures (e.g., transformer-based models, graph neural networks) for data mining.
• Federated learning, edge computing, and distributed frameworks for scalable data processing.
• Techniques for enhancing explainability, robustness, and fairness in AI-driven systems.
2. Applications in specific domains:
• Healthcare: AI-driven diagnostics, personalized medicine, and patient data analytics.
• Finance: Fraud detection, risk assessment, and algorithmic trading via AI.
• Smart cities: IoT data integration, traffic optimization, and urban sustainability.
• Education: Intelligent tutoring systems, dropout prevention, and cognitive modeling.
3. Methodological approaches:
• Hybrid systems combining AI with traditional data mining techniques.
• Real-time data processing for dynamic environments (e.g., industrial IoT).
• Multi-modal data fusion (text, images, sensor data) in AI-driven analytics.
4. Ethical and practical considerations:
• Mitigating bias and ensuring fairness in AI-driven decision-making.
• Data privacy, security, and compliance in large-scale deployments.
• Case studies on operational challenges and success stories.
5. Future trends and perspectives:
• Forecasting the role of AI in the evolution of data mining (e.g., autonomous systems).
• Cross-disciplinary synergies (e.g., AI + quantum computing).
• Speculations on next-generation data processing paradigms.

Dr. Zhonghong Ou
Dr. Kaize Shi
Dr. Qika Lin
Dr. Yifan Zhu
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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• AI-driven intelligent data processing
• data mining
• information fusion
• data intelligence