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
Digital Transformation at the Local Tier of Government in Europe: Dynamics and Effects from a Cross-Countries and Over-Time Comparative Perspective (DIGILOG)
Digital transformation constitutes one of the most important innovations at the local level of government and is expected to reshape local service delivery, public administration, and governance in Europe fundamentally. Most recently, the COVID-19 pandemic has shown the fundamental importance of a well-prepared ...
DISCAP – Digital Infrastructure for Sustainable Consumption and Production (UNEP)
The goal of this project is to lay the technological foundations for empowering various stakeholders to make fact-based decisions to achieve sustainable consumption and production. In particular, we will evaluate how various data science, machine learning and artificial intelligence methods can be used to extract ...
DataInc – Intelligent Data Integration and Cleaning
Clean, reliable data is crucial to an increasingly digitized financial industry. We currently observe a lack of consistent, high-quality data across asset classes which requires costly and time-intensive human intervention. We propose an AI-driven solution to address this issue.
Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum.
CLEF 2016 Conference and Labs of the Evaluation Forum, Évora, Portugal, 5-8 September 2016.
Labs Working Notes ; 1609.
Available from: http://ceur-ws.org/Vol-1609/16091123.pdf
Fasel, Daniel; Andreas, Meier, eds.,
Available from: https://doi.org/10.1007/978-3-658-11589-0_4
Lukic, Yanick; Vogt, Carlo; Dürr, Oliver; Stadelmann, Thilo,
2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP),.
26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), Vietri sul Mare, Italy, 13-16 Sept. 2016.
Available from: https://doi.org/10.1109/MLSP.2016.7738816
Neuhaus, Stephan; Müntener, Roman; Edeline, Korian; Donnet, Benoit; Gubser, Elio,
Proceedings of the 2016 workshop on Applied Networking Research Workshop - ANRW 16.
Applied Networking Research Workshop (ANRW 16), Berlin, 16 July 2016.
Available from: https://doi.org/10.1145/2959424.2959425
Dessimoz, Jean-Daniel; Koehler, Jana; Stadelmann, Thilo,
36(2), pp. 102-105.
Available from: https://doi.org/10.1609/aimag.v36i2.2591