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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Sima, Ana Claudia; Mendes de Farias, Tarcisio; Anisimova, Maria; Dessimoz, Christophe; Robinson-Rechavi, Marc; Zbinden, Erich; Stockinger, Kurt,
2022.
Distributed and Parallel Databases.
40(2), pp. 409-440.
Available from: https://doi.org/10.1007/s10619-022-07414-w
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Holzer, Severin; Stockinger, Kurt,
2022.
Detecting errors in databases with bidirectional recurrent neural networks [paper].
In:
Proceedings of EDBT 2022.
25th International Conference on Extending Database Technology, Edinburgh (online), 29 March - 1 April 2022.
OpenProceedings.
pp. 364-367.
Available from: https://doi.org/10.48786/edbt.2022.22
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Klingler, Yasamin; Lehmann, Claude; Monteiro, Joao Pedro; Saladin, Carlo; Bernstein, Abraham; Stockinger, Kurt,
2022.
Evaluation of algorithms for interaction-sparse recommendations : neural networks don’t always win [paper].
In:
Proceedings of EDBT 2022.
25th International Conference on Extending Database Technology, Edinburgh (online), 29 March - 1 April 2022.
OpenProceedings.
pp. 475-486.
Available from: https://doi.org/10.48786/edbt.2022.42
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Lehmann, Claude; Gehrig, Dennis; Holdener, Stefan; Saladin, Carlo; Monteiro, João Pedro; Stockinger, Kurt,
2022.
Building natural language interfaces for databases in practice [paper].
In:
Proceedings of the 34th SSDBM.
34th International Conference on Scientific and Statistical Database Management (SSDBM), Copenhagen, Denmark, 6 - 8 July 2022.
Association for Computing Machinery.
Available from: https://doi.org/10.1145/3538712.3538744
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Amer-Yahia, Sihem; Koutrika, Georgia; Braschler, Martin; Calvanese, Diego; Lanti, Davide; Lücke-Tieke, Hendrik; Mosca, Alessandro; Mendes de Farias, Tarcisio; Papadopoulos, Dimitris; Patil, Yogendra; Rull, Guillem; Smith, Ellery; Skoutas, Dimitrios; Subramanian, Srividya; Stockinger, Kurt,
2021.
INODE : building an end-to-end data exploration system in practice.
SIGMOD Record.
50(4), pp. 23-29.
Available from: https://doi.org/10.21256/zhaw-23624