Towards Real-Time Data Ingestion for Industrial Internet of Things

Since the widespread applications of Internet of Things, the data generated by various devices (e.g., industrial sensors) in real-time is rapidly growing, providing new opportunities by discovering knowledge from these data to achieve industrial intelligence. However, the problem of how to efficiently collect, process, and store the massive data generated by a large amount of different devices challenges existing systems. Motivated by this, we develop KTC, a real-time data ingestion system for the Industrial Internet of Things that features many advantages, including high throughput, life-long storage, horizontal scaling, ease of use and extend, and so on. In this paper, we introduce the architecture and key techniques of KTC, and describe the key demonstration scenarios of our system.