Digital monitoring facilities including accelerators, light sources, cameras, environmental sensors, and medical devices produce large volumes of streaming data. The streaming data needs to be analyzed in reactive real-time or processed in near real-time to enable next-generation scientific discoveries. There has been an explosion of new research and technologies for stream analytics from the academic and industry sectors to address the growing data volumes. Existing scientific programming techniques have demonstrated their ability to manage complex processes in the development and operation of safety-critical systems.

However, there is still an emerging need to improve the existing computational science and programming methods to efficiently manage intensive data streams in the systems. Artificial intelligence in the form of deep learning networks has demonstrated extreme power and success in a series of applications. However, the application of such networks in mission-critical situations such as medicine, military, and self-driving vehicles is questionable. In contrast, in scientific computing and programming applications, a robust and accurate framework of analysis has been established that often guarantees safe application with high precision.

In this Special Issue, we will explore the improvement of scientific programming methods and deep neural networks to respectively enhance their flexibility and robustness in data stream analysis. We will deliver more advanced and intelligent methods taking advantage of the existing large stream datasets. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Advances in programming for sensor data stream management
• Software engineering for sensor data stream management
• Services computing for sensor data stream management
• Combination and improvement of traditional scientific programming and deep learning techniques for sensor data stream analysis
• Deep learning programming for sensor data stream analysis
• Concept drift processing in sensor data streams
• Anomaly detection and classification for sensor data streams
• Root cause analysis based on sensor data streams
• Advances in scientific programming in video and image processing
• Advances in artificial intelligence in video and image processing
• Security and privacy programming for sensor data streams
• Blockchain programming for data stream management