Christoph F. Eick
- Artificial Intelligence top 5%
- Information Systems top 5%
- Signal Processing top 5%
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Zenghua ZhaoWei DingRicardo VilaltaJean‐Philippe NicotSujing WangXiaojing YuanT. F. StepinskiPeter C. Lockemann
- Topics
- Data Management and Algorithms (24 papers)Data Mining Algorithms and Applications (17 papers)Advanced Database Systems and Queries (15 papers)
- Journals
- Information SciencesIEEE Transactions on Knowledge and Data EngineeringComputers & Geosciences
- Partner nations
- United StatesFranceQatar
In The Last Decade
Christoph F. Eick
71 papers receiving 534 citations
Peers
Comparison fields: 5 of 92
- Artificial Intelligence 313
- Information Systems 184
- Signal Processing 162
- Computer Networks and Communications 99
- Computer Vision and Pattern Recognition 91
Countries citing papers authored by Christoph F. Eick
This map shows the geographic impact of Christoph F. Eick'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 Christoph F. Eick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christoph F. Eick more than expected).
Fields of papers citing papers by Christoph F. Eick
This network shows the impact of papers produced by Christoph F. Eick. 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 Christoph F. Eick. The network helps show where Christoph F. Eick may publish in the future.
Co-authorship network of co-authors of Christoph F. Eick
This figure shows the co-authorship network connecting the top 25 collaborators of Christoph F. Eick. A scholar is included among the top collaborators of Christoph F. Eick based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Christoph F. Eick. Christoph F. Eick is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 8 | |
| 6 | 8 | |
| 7 | Online Learning of Spacecraft Simulation Models | 1 |
| 8 | 42 | |
| 9 | 94 | |
| 10 | Piece-wise model fitting using local data patterns | 3 |
| 11 | K-medoid-style Clustering Algorithms for Supervised Summary Generation. | 4 |
| 12 | 0 | |
| 13 | 0 | |
| 14 | Deriving queries from results using genetic programming | 1 |
| 15 | MASSON: discovering commonalities in collection of objects using genetic programming | 6 |
| 16 | 16 | |
| 17 | A Methodology for the Design and Transformation of Conceptual Schemas | 17 |
| 18 | 1 | |
| 19 | Computer bridge—a challenge for AI | 3 |
| 20 | 3 |
About Christoph F. Eick
Christoph F. Eick is a scholar working on Signal Processing, Artificial Intelligence and Information Systems, having authored 77 papers that have together received 575 indexed citations. Recurring topics across this work include Data Management and Algorithms (24 papers), Data Mining Algorithms and Applications (17 papers) and Advanced Database Systems and Queries (15 papers). The work is most often cited by research in Signal Processing (162 citations), Artificial Intelligence (313 citations) and Information Systems (184 citations). Christoph F. Eick has collaborated with scholars based in United States, France and Qatar. Frequent co-authors include Zenghua Zhao, Wei Ding, Ricardo Vilalta, Jean‐Philippe Nicot, Sujing Wang, Xiaojing Yuan, T. F. Stepinski, Peter C. Lockemann, Jing Wang and Vadeerat Rinsurongkawong. Their work appears in journals such as Information Sciences, IEEE Transactions on Knowledge and Data Engineering and Computers & Geosciences.
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.