Reference projects

iPRODICT – The Intelligent Factory

iPRODICT develops an intelligent approach to semi-automate the adaptation and improvement of business processes. Historic process information as well as real-time sensor data are used to predict the optimal process execution. Big Data analytics allows to adapt the process to the current context. An application scenario in the steel processing industry highlights the impacts of the approach in the areas of Industry 4.0 and factories of the future.

Project duration: 

September 2014 – August 2017

Link to the project page(german)

SAKE – Semantic Analysis of Complex Events

SAKE develops a modular framework for processing event data. A first application area is concerned with the timely recognition and prediction of operating flaws in production systems. A second application area deals with monitoring and detection of failures in IT networks. We will apply and evaluate semantic web standards for storing data suitable for machine learning.

Project duration: 

December 2014 – November 2017

Link to the project Page

FERARI – Flexible Event Processing for Big Data Architectures

FERARI paves the way for efficient and timely processing of Big Data. The systematic approach will enable leveraging recent advances in in-situ processing algorithms, which perform much of the processing at the source where the data is generated. By diminishing the need for large centralized infrastructures, huge data transfers, and the respective necessary energy, in-situ processing lowers the cost of Big Data stream processing systems by orders of magnitude. Similarly, huge acceleration is obtained in performing real-time knowledge extraction and monitoring. Applications include the analysis of M2M data and streaming fraud detection in telecommunications.

Project duration:

February 2014 – January 2017

Link to the project page