English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we present the current position of the research projectML-Quadrat, which aims to extend the methodology, modeling language and toolsupport of ThingML - an open source modeling tool for IoT/CPS - to addressMachine Learning needs for the IoT applications. Currently, ThingML offers amodeling language and tool support for modeling the components of the system,their communication interfaces as well as their behaviors. The latter is donethrough state machines. However, we argue that in many cases IoT/CPS servicesinvolve system components and physical processes, whose behaviors are not wellunderstood in order to be modeled using state machines. Hence, quite often adata-driven approach that enables inference based on the observed data, e.g.,using Machine Learning is preferred. To this aim, ML-Quadrat integrates thenecessary Machine Learning concepts into ThingML both on the modeling level(syntax and semantics of the modeling language) and on the code generatorslevel. We plan to support two target platforms for code generation regardingStream Processing and Complex Event Processing, namely Apache SAMOA and Apama.

ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning