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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DECIDE - Digital Enabling of Circularity, Innovation, Development and Environment (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 ...
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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 ...
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DataInc – Intelligent Data Integration and Cleaning
Clean, reliable data is crucial to an increasinglydigitized financial industry. We currently observe alack of consistent, high-quality data across assetclasses which requires costly and time-intensivehuman intervention. We propose an AI-drivensolution to address this issue.
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Smith, Ellery; Paloots, Rahel; Giagkos, Dimitris; Baudis, Michael; Stockinger, Kurt,
2024.
Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines.
Bioinformatics Advances.
Available from: https://doi.org/10.1093/bioadv/vbae045
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Frehner, Robin; Wu, Kesheng; Sim, Alexander; Kim, Jinoh; Stockinger, Kurt,
2024.
Detecting anomalies in time series using kernel density approaches.
IEEE Access.
12, pp. 33420-33439.
Available from: https://doi.org/10.1109/ACCESS.2024.3371891
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Lehmann, Claude; Sulimov, Pavel; Stockinger, Kurt,
2024.
Is your learned query optimizer behaving as you expect? : a machine learning perspective.
Proceedings of the VLDB Endowment.
17(7), pp. 1565-1577.
Available from: https://doi.org/10.21256/zhaw-30586
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Zhang, Yi; Deriu, Jan Milan; Katsogiannis-Meimarakis, George; Kosten, Catherine; Koutrika, Georgia; Stockinger, Kurt,
2024.
ScienceBenchmark : a complex real-world benchmark for evaluating natural language to SQL systems.
Proceedings of the VLDB Endowment.
17(4), pp. 685-698.
Available from: https://doi.org/10.14778/3636218.3636225
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Kosten, Catherine; Cudré-Mauroux, Philippe; Stockinger, Kurt,
2024.
Spider4SPARQL : a complex benchmark for evaluating knowledge graph question answering systems [paper].
In:
2023 IEEE International Conference on Big Data (BigData).
IEEE International Conference on Big Data, Sorrento, Italy, 15-18 December 2023.
IEEE.
Available from: https://doi.org/10.1109/BigData59044.2023.10386182