AI-Oriented Data Engineering Solutions for Large-Scale Industrial IoT Applications
Keywords:
Industrial Internet of Things (IIoT), AI-oriented data engineering, Predictive maintenance, Anomaly detection, Distributed computing, Real-time data processing.Abstract
The advent of Industrial Internet of Things (IIoT) has revolutionized modern industries by enabling
real-time monitoring, data collection, and automation across large-scale operations. However, the
sheer volume, velocity, and variety of data generated in IIoT ecosystems present significant
challenges for data processing and analytics. This paper explores AI-oriented data engineering
solutions tailored to the demands of large-scale IIoT applications. By integrating machine learning
models, distributed computing frameworks, and data pipelines, this study proposes a
comprehensive approach to optimizing data management and predictive analytics in industrial
environments. The solutions focus on data preprocessing, real-time stream processing, anomaly
detection, and predictive maintenance, offering strategies to enhance operational efficiency,
reliability, and scalability in IIoT systems. Key findings demonstrate that AI-driven data
engineering methods significantly reduce latency, improve predictive model accuracy, and support
better decision-making processes. The proposed framework underscores the role of artificial
intelligence in transforming data engineering practices to meet the complex requirements of largescale IIoT networks.
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