Knowledge-Based Technology Area
Research in the Area Knowledge-Based Technology (KBT) targets at the exploitation and application of state-of-the-art and new knowledge-based and data-driven methods for supporting the process of managing, controlling and applying complex software, hence improving software quality in terms of interpretability, operability and optimization.
SCCH’s Knowledge-Based Technology (KBT) Area represents the view of software as an artifact capable of generating, representing and processing knowledge rather than just dealing with data and information.
The importance of knowledge-based technologies increases with the amount and complexity of available data. KBT’s orientation is primarily mathematical and formal, and its R&D concentrates on uniquely combining statistical methods (e.g., machine learning and kernel-based techniques) and cognitive approaches (e.g., fuzzy rule-based methods and natural language processing) from the field of computational intelligence in order to solve modeling, optimization and prediction problems in industry.
KBT has three core competencies:
Machine vision remains one of the most important key technologies for productivity, innovation, quality and cost efficiency for various industrial branches. In particular,
Knowledge-Based Vision Systems for Industrial Applications (KVS) aims at leveraging knowledge-based technologies to solve machine vision problems related to texture analysis and object recognition.
For automated and intelligent data analysis,
Industrial Data Mining (IDM) deals with novel solutions that aim at optimization, prediction and knowledge discovery for higher-level process understanding in the fields of process engineering, manufacturing and machine building and in environmental technologies.
Biomedical data analysis (BDA) belongs to the sector of medical technologies and pharmaceutics. BDA aims to develop techniques of comparative data analysis for data collection containing bio-molecular information and for spatio-temporal data.