Knowledge and Service Engineering
Description of Knowledge and Service Engineering
We are specialists in knowledge representation and management, while implementing information systems, or data integration systems, regardless of their domain.
Semantic data modeling: ontologies, conceptual models, business rule detection and metamodeling.
Automated reasoning: inference of new data from existing data, generation of valid test data.
Integration: integration of data or systems.
Requirements engineering: detection of user needs both in terms of data and processes.
Service engineering and business process management: design and development of solutions for service and process management.
Web technologies: design and development of web-based applications.
Knowledge and Service Engineering Success Stories
In this project, the inLab FIB team has worked jointly with the Barcelona Innovative Transportation (BIT) research group of the ETSECCPB school in the development of a web solution for HPC task submission within the framework of the European E-Med project.
The inLab FIB will carry out a study and develop a proof of concept of a new triage system for the “Hospital de la Santa Creu i Sant Pau”. This system will prioritize certain patients to prevent waitings from negatively affecting them.
SEAT has contacted inLab FIB and LaCàN to raise the possibility of developing a joint project to scale the use of ARCO and the ePGD library, currently validated for simple models, to solve NVH parametric problems with a realistic car geometry.
SUDOQU (enSUring Data with Ontological QUality) is a research project funded by the Ministry of Science and Innovation, the objective of which is the development of techniques to ensure data quality through the use of ontologies.
Foxtenn Technologies is a technology company specialized in sporting events. The company has created a technology capable of determining with maximum precision the bounce of the ball.
The objective of this project is to analyze the circulation time between the different significant points of the network.