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
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FarmAI – Artificial intelligence for Farming Simulator
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Market Data Detective
SIX Financial Information AG is a financial data vendor. Billions of price messages are processed and delivered to clients by SIX every day. In order to improve market data quality, project "Market Data Detective" aims at developing and evaluating the effectiveness of new, mostly automated and innovative data ...
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Libra: A One-Tool Solution for MLD4 Compliance
Compared with earlier regulations, the 4th European Money Laundering Directive (MLD4) imposes rigorously increased requirements. It compels obliged entities to conduct in depth screenings of customers and their associations. The Libra Project aims at providing a one tool solution for meeting MLD4 compliance. The ...
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Kittelmann, Florian; Sulimov, Pavel; Stockinger, Kurt,
2024.
QardEst : using quantum machine learning for cardinality estimation of join queries [paper].
In:
1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-30917
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Schmitt-Koopmann, Felix; Huang, Elaine M.; Hutter, Hans-Peter; Stadelmann, Thilo; Darvishy, Alireza,
2024.
MathNet : a data-centric approach for printed mathematical expression recognition.
IEEE Access.
12, pp. 76963-76974.
Available from: https://doi.org/10.1109/ACCESS.2024.3404834
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Gerber, Jonathan; Saxer, Jasmin S.; B. Kreiner, Bruno; Weiler, Andreas,
2024.
DIGILOG : towards a monitoring platform for digital transformation of European communities.
In:
Joint Proceedings of RCIS 2024 Workshops and Research Projects Track.
18th International Conference on Research Challenges in Information Science (RCIS), Guimarães, Portugal, 14-17 May 2024.
RWTH Aachen University.
Available from: https://doi.org/10.21256/zhaw-30792
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Chen, Yaxuan; Vergara, Ana Fernandez; Hamilton, Angus; Stockinger, Kurt,
2024.
Digital public infrastructure for environmental sustainability.
United Nations Environment Programme.
ISBN 978-92-807-4157-5.
Available from: https://doi.org/10.21256/zhaw-30874
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Appel, Jan; Weiler, Andreas,
2024.
XCrowd : a realistic crowd simulation tool for efficient movement management.
In:
Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conference.
6th International Workshop on Big Mobility Data Analytics (BMDA) during EDBT/ICDT Joint Conference, Paestum, Italy, 25 March - 28 March 2024.
Aachen:
RWTH Aachen University.
Available from: https://doi.org/10.21256/zhaw-30720