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
Smart UMH: Smart urban multihub concept: Sustainable and liveable cities with low logistics visibility
Cities suffer from too much traffic, leading to congestion, air and noise pollution. Increased e-commerce popularity intensifies these challenges further. The Covid crisis has proven that our urban logistics systems are neither reliable, resilient, nor sustainable. Our objective is to develop a future urban ...
Digital Transformation at the Local Tier of Government in Europe: Dynamics and Effects from a Cross-Countries and Over-Time Comparative Perspective (DIGILOG)
Background: 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 ...
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 ...
Taranova, Anastasia; Braschler, Martin,
Advances in Information Retrieval.
43rd European Conference on Information Retrieval, Lucca (online), 28 March - 1 April 2021.
Lecture Notes in Computer Science ; 12657.
Available from: https://doi.org/10.1007/978-3-030-72240-1_42
Stadelmann, Thilo; Würsch, Christoph,
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-20885
Tuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar; Schilling, Frank-Peter; Stadelmann, Thilo,
1(4), pp. 510-538.
Available from: https://doi.org/10.3390/ai1040031
Tuggener, Lukas; Satyawan, Yvan Putra; Pacha, Alexander; Schmidhuber, Jürgen; Stadelmann, Thilo,
Proceedings of the 25th International Conference on Pattern Recognition 2020 (ICPR’20).
25th International Conference on Pattern Recognition 2020 (ICPR’20), Online, 10-15 January 2021.
Available from: https://doi.org/10.21256/zhaw-20647
Glüge, Stefan; Amirian, Mohammadreza; Flumini, Dandolo; Stadelmann, Thilo,
Schilling, Frank-Peter; Stadelmann, Thilo, eds.,
Artificial Neural Networks in Pattern Recognition.
9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020.
Lecture Notes in Computer Science ; 12294.
Available from: https://doi.org/10.1007/978-3-030-58309-5_10