Stream Analytics in IoT Mashup Tools

Abstract

Consumption of data streams generated from IoT devices during IoT application development is gaining prominence as the data insights are paramount for building high-impact applications. IoT mashup tools, i.e. tools that aim to reduce the development effort in the context of IoT via graphical flow-based programming, suffer from various architectural limitations which prevent the usage of data analytics as part of the application logic. Moreover, the approach of flow-based programming is not conducive for stream processing. We introduce our new mashup tool aFlux based on actor system with concurrent and asynchronous execution semantics to overcome the prevalent architectural limitations and support in-built user-configurable stream processing capabilities. Furthermore, parametrizing the control points of stream processing in the tool enables non-experts to use various stream processing styles and deal with the subtle nuances of stream processing effortlessly. We validate the effectiveness of parametrization in a real-time traffic use case.