Development of Big Data System with API Authentication for Industrial Boilers
Youngmin Joo, Seunghyeon Park, Hyun Woo, Kiwoong Kwon · 2024
Recent advancements in AI are being recognized as a promising solution to improve the efficiency of industrial boilers. However, high-performance AI models require extensive amounts of training data. To address this, this paper proposes a big data system designed for efficient data collection, processing, storage, and sharing. It enables fast data storage and sharing through distributed data processing, while protecting sensitive data via authentication. Additionally, it provides a data sharing interface that minimizes unnecessary data transfers and reduces delays in additional authentication procedures through caching. Performance evaluation has confirmed that the proposed system delivers high throughput for write operations and reasonable latency for read operations, even under massive user requests.