ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning
This addresses the need for machine learning integration in IoT applications, but it is incremental as it builds on an existing tool without demonstrating new performance gains.
The paper extends ThingML, a model-driven software engineering tool for IoT/CPS, to integrate machine learning concepts, enabling data-driven inference for components with poorly understood behaviors, with plans to support Apache SAMOA and Apama for code generation.
In this paper, we present the current position of the research project ML-Quadrat, which aims to extend the methodology, modeling language and tool support of ThingML - an open source modeling tool for IoT/CPS - to address Machine Learning needs for the IoT applications. Currently, ThingML offers a modeling language and tool support for modeling the components of the system, their communication interfaces as well as their behaviors. The latter is done through state machines. However, we argue that in many cases IoT/CPS services involve system components and physical processes, whose behaviors are not well understood in order to be modeled using state machines. Hence, quite often a data-driven approach that enables inference based on the observed data, e.g., using Machine Learning is preferred. To this aim, ML-Quadrat integrates the necessary Machine Learning concepts into ThingML both on the modeling level (syntax and semantics of the modeling language) and on the code generators level. We plan to support two target platforms for code generation regarding Stream Processing and Complex Event Processing, namely Apache SAMOA and Apama.