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
FWA: Visual Food Waste Analysis for Sustainable Kitchens
A novel approach for a fully automated food waste management solution for commercial kitchens is investigated. Food waste is automatically detected using a new camera device, preprocessed in real-time and classified using machine learning algorithms.
NQuest – Natural Language Query Exploration System
There is a huge amount of valuable information hidden in a company's database which is not easily accessible to business people. To query these databases, end users need to know the technical query language SQL as well as the database structure. However, typical end users do not have enough SQL skills to formulate ...
Feasibility Study Reinforcement Learning for Heating Systems
Proceedings of the 33rd SSDBM.
International Conference on Scientific and Statistical Database Management (SSDBM), Online, 6-7 July 2021.
Available from: https://doi.org/10.1145/3468791.3469119
Brunner, Ursin; Stockinger, Kurt,
Proceedings of the 37th ICDE.
International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021.
Available from: https://doi.org/10.21256/zhaw-22000
Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020.
1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.
Available from: https://doi.org/10.21256/zhaw-22061
Journal of Big Data.
Available from: https://doi.org/10.1186/s40537-020-00383-w
Aydarkhanov, Ruslan; Ušćumlić, Marija; Chavarriaga, Ricardo; Gheorghe, Lucian; Millán, José del R,
Journal of Neural Engineering.
18(2), pp. 026010.
Available from: https://doi.org/10.1088/1741-2552/abdfb2