Intelligent Information Systems
We Derive Value from Data and Information
- 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 of the InIT 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 research 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.
Research Topics
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:
Big Data and Nano Data
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:
Current research:
- Information retrieval for small document collections
- Machine learning for query optimization
- Artificial intelligence for data integration and cleaning
- Quantum databases and quantum machine learning
Data Understanding
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.
Current research:
- 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
As part of the reorganization of the research database, the previous lists of research projects are no longer available. Die Zukunft geht in Richtung Volltextsuche und Filterung, um bestmögliche Suchergebnisse für unsere Besucher:innen zur Verfügung zu stellen.
In the meantime, you can easily find the projects via text search using the following link: «To the new search in the project database»
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Tuggener, Lukas; Elezi, Ismail; Schmidhuber, Jürgen; Pelillo, Marcello; Stadelmann, Thilo,
2018.
DeepScores : a dataset for segmentation, detection and classification of tiny objects [paper].
In:
2018 24th International Conference on Pattern Recognition (ICPR).
24th International Conference on Pattern Recognition (ICPR 2018), Beijing, China, 20-28 August 2018.
IEEE.
pp. 1-6.
Available from: https://doi.org/10.1109/ICPR.2018.8545307
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Stockinger, Kurt; Heitz, Jonas; Bundi, Nils Andri; Breymann, Wolfgang,
2018.
Large-scale data-driven financial risk modeling using big data technology [paper].
In:
2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT).
5th International Conference on Big Data Computing, Applications and Technologies (BDCAT), Zurich, Switzerland, 17-20 December 2018.
IEEE.
pp. 206-207.
Available from: https://doi.org/10.1109/BDCAT.2018.00033
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Meier, Benjamin Bruno; Elezi, Ismail; Amirian, Mohammadreza; Dürr, Oliver; Stadelmann, Thilo,
2018.
Learning neural models for end-to-end clustering [paper].
In:
Artificial Neural Networks in Pattern Recognition.
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018.
Springer.
pp. 126-138.
Lecture Notes in Computer Science ; 11081.
Available from: https://doi.org/10.1007/978-3-319-99978-4_10
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Hibraj, Feliks; Vascon, Sebastiano; Stadelmann, Thilo; Pelillo, Marcello,
2018.
Speaker clustering using dominant sets [paper].
In:
2018 24th International Conference on Pattern Recognition (ICPR).
24th International Conference on Pattern Recognition (ICPR 2018), Beijing, China, 20-28 August 2018.
IEEE.
pp. 3549-3554.
Available from: https://doi.org/10.1109/ICPR.2018.8546067
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Deriu, Jan Milan; Cieliebak, Mark,
2018.
Syntactic manipulation for generating more diverse and interesting texts [paper].
In:
Proceedings of the 11th International Conference on Natural Language Generation.
11th International Conference on Natural Language Generation (INLG 2018), Tilburg, The Netherlands, 5-8 November 2018.
Association for Computational Linguistics.
pp. 22-34.
Available from: https://doi.org/10.21256/zhaw-4875