Andreas Wierse
Impact in
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- Data Visualization and Analytics
- Signal Processing top 10%
- Data Management and Algorithms
- Time Series Analysis and Forecasting
Papers in
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- Distributed and Parallel Computing Systems 1
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- Data Mining Algorithms and Applications 1
- Co-authors
- Usama M. Fayyad (1 shared paper)Georges Grinstein (1 shared paper)Martin Rumpf (1 shared paper)Till Riedel (1 shared paper)Peter J. Haas (1 shared paper)Ulrich Lang (1 shared paper)
- Journals
- Future Generation Computer Systems (1 paper)Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1 paper)Morgan Kaufmann Publishers Inc. eBooks (1 paper)
- Partner nations
- GermanyFranceUnited States
In The Last Decade
Andreas Wierse
6 papers receiving 298 citations
Peers
Comparison fields: 5 of 96
- Computer Vision and Pattern Recognition 141
- Signal Processing 72
- Computer Science Applications 21
- Information Systems 83
- Artificial Intelligence 112
Countries citing papers authored by Andreas Wierse
This map shows the geographic impact of Andreas Wierse's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Andreas Wierse with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Wierse more than expected).
Fields of papers citing papers by Andreas Wierse
This network shows the impact of papers produced by Andreas Wierse. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Andreas Wierse. The network helps show where Andreas Wierse may publish in the future.
Co-authors
The 6 scholars most cited alongside Andreas Wierse, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Information Visualization in Data Mining and Knowledge Discovery | 2001 | 320 |
| 2 | 1995 | 11 | |
| 3 | GRAPE. Eine objektorientierte Visualisierungs- und Numerikplattform | 1992 | 5 |
| 4 | 2017 | 3 | |
| 5 | What can visualization do for data mining | 2001 | 1 |
| 6 | 1995 | 1 |
About Andreas Wierse
Andreas Wierse is a scholar working on Computer Networks and Communications, Information Systems, Management Science and Operations Research, Aerospace Engineering and Building and Construction, having authored 6 papers that have together received 341 indexed citations. Recurring topics across this work include Distributed and Parallel Computing Systems (1 paper), Simulation Techniques and Applications (1 paper), Data Mining Algorithms and Applications (1 paper), 3D Modeling in Geospatial Applications (1 paper) and Air Traffic Management and Optimization (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (141 citations), Signal Processing (72 citations), Computer Science Applications (21 citations), Information Systems (83 citations) and Artificial Intelligence (112 citations). Andreas Wierse has collaborated with scholars based in Germany, France and United States. Frequent co-authors include Usama M. Fayyad, Georges Grinstein, Martin Rumpf, Till Riedel, Peter J. Haas and Ulrich Lang. Their work appears in journals such as Future Generation Computer Systems, Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE and Morgan Kaufmann Publishers Inc. eBooks.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.