HANNOVER MESSE 2018, 23 - 27 April
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Logo Software Competence Center

Software Competence Center

Logo Software Competence Center
Exhibition stand
Hall 2, Stand A44
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Co-exhibitor with
Upper Austrian Research

Company Data

Software Competence
Center Hagenberg GmbH

Softwarepark 21
4232 Hagenberg
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Fax: +43 7236 3343888


Mr. Dr. Thomas Ziebermayr
Executive Head Software Analytics and Evolution
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Mr. Mag. Manfred Schwanthaler
Business Development
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Mr. Dr. Bernhard Freudenthaler
Executive Head Data Analysis Systems
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Type of company

Year founded

No. of employees
51-100 (Status: 2016)

Annual turnover
between 1M and 10M EUR (Status: 2016)

Dr. Klaus Pirklbauer (CEO)
Prof. Dr. A. Min Tjoa. (CSO)

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Company profile

Software Competence Center Hagenberg (SCCH) is an internationally recognized research organization that drives innovation in the production and application of software by integrating basic research in the solution of complex application problems.
You will find the following 4 research areas with their research topics:
1) Knowledge-Based Vision Systems (KVS): Object-Recognition, Object-Tracking, Connecting different Sensors, High Performance Computing, Embedded Systems.
2) Data Analysis Systems (DAS) Data Analytics, Big Data, Data Management, Optimization, Prognosis & Forecast.
3) Software Analytics and Evolution (SAE): Model-Based-Testing, Domain-Specific-Languages, Knowledge-Extraction from Source Code, Software-Maintenance.
4) Rigorous Methods in Software Engineering (RSE): Model-based Systems Engineering, Formal Methods, Process-Mining, Abstract State Machines, Business Process Modeling.

Company website
Video SCCH Company Video
0:50 min Exhibitor Video



Augmented Reality in Industry

Welding jobs that are not automated often pose particular challenges in their execution, due to not only the geometry and position of a welding bead to be produced but frequently also the required job steps and precision of execution. For small series production, this means that much more information more

Digital Twin

Rigorous Methods in Software Engineering (RSE) supports the use of formal/rigorous methods for the production of correct, reliable software as well as for model-based system development. With their correct usage, rigorous methods complement todays software development methods and enable precise modeling more

Deep Learning

The SCCH develops image-supported analysis systems for the measurement and evaluation of optical patterns as well as spatial-temporal motion data. Here we typically combine various analysis methods: real-time capable image processing on the level of raw data, deep learning for extraction of semantic more

Rulebook Generator

Wissen aus Software generierenread more

Humans in Industry 4.0

Industry 4.0 (the smart factory) is usually equated with networking and improved flexibility. However, an important focus is the integration of humans in this new work environment. Those tasks where humans continue to be an important factor must be supported by machines. This elevates the importance more

Robust Embedded Visual Sensor Systems

Embedded visual sensor systems need to be literally robust not only because of their application domains. The robustness of the methods and algorithms also plays an important role in an environment with frequent interference, e.g. the noise as seen in the grayscale image on top. Increasingly, smart more


INDYCO (Integrated Dynamic Decision Support System Component for Disaster Management) is a decision support system which can 1) react rapidly and dynamically to changing situations during a disaster and 2) handle new disasters for which no contingency plan exists. The INDYCO system analysed different more

Prozess Mining for Industry

Processes are the heart of an enterprise and describe the interconnections of tasks in day-to-day operations. Modeling, analysis and improvement of these processes has been a research interest for many decades and for some years a focus of research and industrial projects at Software Competence Center more

Predictive Analytics

The term Industry 4.0 (the smart factory) has become a buzzword. Often huge data streams (e.g., machine data, process data, quality data) from diverse, heterogeneous data sources must be linked and analyzed in order to provide a useful basis for decision support and recommended actions for humans. The more

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