StreamPipes Connect: Semantics-Based Edge Adapters for the IIoT

Abstract

Accessing continuous time series data from various machines and sensors is a crucial task to enable data-driven decision making in the Industrial Internet of Things (IIoT). However, connecting data from industrial machines to real-time analytics software is still technically complex and time-consuming due to the heterogeneity of protocols, formats and sensor types. To mitigate these challenges, we present StreamPipes Connect, targeted at domain experts to ingest, harmonize, and share time series data as part of our industry-proven open source IIoT analytics toolbox StreamPipes. Our main contributions are (i) a semantic adapter model including automated transformation rules for pre-processing, and (ii) a distributed architecture design to instantiate adapters at edge nodes where the data originates. The evaluation of a conducted user study shows that domain experts are capable of connecting new sources in less than a minute by using our system. The presented solution is publicly available as part of the open source software Apache StreamPipes.