Intelligent Information Systems
- How to leverage information?
- How to find new topics and trends?
- How to derive insight from heterogeneous/unstructured data and information?
- How to allow a «natural» access to data?
- How can software link data automatically?
These are but a few of the questions that the Intelligent Information Systems (IIS) group is working to answer. While the “data and information flood” is often discussed negatively, we see a great opportunity to leverage data and information using the right approaches – both at search-time, as well as during analysis.
The group transfers insights derived from research and development into teaching for students of the computer science curricula. It offers modules such as “Information Engineering 1 (Information Retrieval)”, “Information Engineering 2 (Data Warehousing & Big Data)” and "Databases". The group is active in both national and international research projects of the EU framework programs.
The Intelligent Information Systems group develops solutions for a changing, data-driven world. It performs research at the intersection of databases (DB), information retrieval (IR), data engineering (DE), natural language processing (NLP) and machine learning (ML)
The group covers two main research lines:
We solve challenging problems when working with a range of datasets from very small (nano data) to very large (big data), where the nature of the problems change drastically as we work on different scales:
- Information retrieval for small document collections
- Machine learning for query optimization
- Artificial intelligence for data integration and cleaning
- Quantum databases and quantum machine learning
As we strive for "intelligent" solutions to data-driven problems, classical information systems need to process data at a different level, interpreting it to gain important information. Both structured and unstructured data must be processed not on a mechanical, but on a semantic level - e.g. by using natural language processing and understanding. Data is ultimately connected through graph structures or made accessible via semantic search.
- Natural language interfaces for databases
- Semantic search on entities
- Knowledge graph construction
- Question answering over knowledge graphs
- Stream analytics and event detection
- Information retrieval evaluation
Synthetic data generation of CoVID-19 CT/X-rays images for enabling fast triage of healthy vs. unhealthy patients
The automatic analysis of X-ray/CT images through artificial intelligence models can be useful to automate the clinical scanning procedure. Nonetheless, the limited access to real COVID patient data leads to the need of synthesizing image samples. The goal of this project is to use existing CT/X-ray image datasets ...
Predictive replenishment of urban distribution centres for the decentralised food supply
DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes
Project DIR3CT aims at improving the image quality of CBCT images by deep learning (DL) the 3D reconstruction from X-ray images end-to-end. This enables a novel CBCT product to be used during radiation therapy and will allow the use of these images for adaptive treatment.
Jao, Ping-Keng; Chavarriaga, Ricardo; Dell'Agnola, Fabio; Arza, Adriana; Atienza, David; Millan, Jose del R.,
IEEE Transactions on Human-Machine Systems.
51(2), pp. 99-108.
Available from: https://doi.org/10.1109/THMS.2020.3038339
Jao, Ping-Keng; Chavarriaga, Ricardo; Millan, Jose del R.,
IEEE Transactions on Affective Computing.
Available from: https://doi.org/10.1109/TAFFC.2021.3059688
Available from: https://doi.org/10.1016/j.is.2021.101938
Candan, K. Selçuk; Ionescu, Bogdan; Goeuriot, Lorraine; Larsen, Birger; Müller, Henning; Joly, Alexis; Maistro, Maria; Piroi, Florina; Faggioli, Gugliemlo; Ferro, Nicola, eds.,
Experimental IR Meets Multilinguality, Multimodality, and Interaction.
12th International Conference of the CLEF Association (CLEF 2021), virtual event, 21–24 September 2021.
Lecture Notes in Computer Science ; 12880.
Available from: https://doi.org/10.1007/978-3-030-85251-1_10
Chavarriaga, Ricardo; Carey, Carole; Contreras-Vidal, Jose Luis; McKinney, Zach; Bianchi, Luigi,
IEEE Open Journal of Engineering in Medicine and Biology.
2, pp. 71-73.
Available from: https://doi.org/10.1109/OJEMB.2021.3061328