Special Issue "AI-Powered Data Management and Analysis for Cyber-Physical-Systems"

Dear Colleagues,

The tight coupling between the cyber and physical worlds is promoting the accumulation of huge amounts of data from multiple areas, which could be appropriately managed and leveraged to improve the performance of CPSs (cyber-physical systems). However, the increasing scale and complexity of big CPS data make it a non-trivial task to cope with prospective failures and performance problems in data management. Moreover, they also raise an urgent need for efficient big data analytic methods, to provide a deeper understanding and better decision making based on the distributed and large-scale CPS data.

In view of these challenges, artificial intelligence (AI) and machine learning (ML) have found ways to improve the reliability and security of CPS data management, as well as improve the performances of CPS applications. In particular, several open source and proprietary solutions have been proposed to meet these requirements, with extensive contributions from industry and academia. However, there remain substantial challenges in AI-powered CPS applications, such as scalable data management, secure data sharing and self-management capabilities, etc.

In this Special Issue, we welcome submissions addressing the underlying challenges and opportunities, presenting novel techniques, experimental results, or theoretical approaches motivated by data management and analytic problems raised in AI-powered CPS applications.

Topics of interest for this Special Issue include but are not limited to the following:

• Intelligent query optimization for big CPS data management;
• Machine-learning-based CPS data analytics;
• Smart analysis, modeling, and visualization of big CPS data;
• Operational analytics and intelligence of big CPS data;
• Anomaly detection and exception handling in CPS;
• Predictive and real-time analytics in CPS;
• Security and privacy of big data in CPS;
• Trust management and threat detection in CPS;
• Other AI-based data management solutions.

You may choose our Joint Special Issue in Systems.

Prof. Dr. Lianyong Qi
Dr. Xiaokang Zhou
Dr. Xuyun Zhang
Guest Editors

Manuscript Submission Information

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