Modularizing Flink programs to enable stream analytics in IoT Mashup tools = Modularización de programas Flink para el análisis de datos en tiempo real en herramientas de Mashup para IOT

Abstract

Among all the challenges that the Internet of Things (IoT) has posed, the analysis of large amounts of information in real time is probably one of the most considerable ones.
To this purpose, novel Big Data approaches foster Stream Analytics as a solution to the strict latency and throughput requirements that IoT platforms impose. Thus, Stream Analytics should constitute an essential component in IoT applications.
However, developing IoT applications is no easy task. To simplify the process of addressing heterogeneous devices at once, mashup tools are widely extended. They provide
a lightweight, user-friendly way to prototyping, but currently they lack integration with platforms for Stream Analytics.
The main goal of this Thesis is to integrate Apache Flink, an open source platform for distributed Stream Analytics, into aFlux, an IoT mashup tool developed at the Chair
of Software and Systems Engineering of the Technical University of Munich. To this end, a conceptual approach to modularize Flink programs in a generic, flexible and
expandable way has been designed, implemented and evaluated. With this approach, end users may not only program Flink graphically, but also get support on how to do it right during the creation of these programs.