Michaela Götz
Impact in
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- Mobile Crowdsensing and Crowdsourcing
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- Data Quality and Management
Papers in
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- Privacy-Preserving Technologies in Data 4
- Algorithms and Data Compression 3
- Internet Traffic Analysis and Secure E-voting 1
- Cryptography and Data Security 1
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- Advanced Database Systems and Queries 4
- Peer-to-Peer Network Technologies 2
- Co-authors
- Johannes E. Gehrke (5 shared papers)Raghav Kaushik (1 shared paper)Arvind Arasu (1 shared paper)Ashwin Machanavajjhala (2 shared papers)Suman Nath (1 shared paper)Xiaokui Xiao (1 shared paper)Guozhang Wang (1 shared paper)Christoph E. Koch (3 shared papers)
- Journals
- Information Systems (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)Proceedings of the VLDB Endowment (1 paper)Lecture notes in computer science (1 paper)International Conference on Management of Data (1 paper)
- Partner nations
- United StatesNorwayGermany
In The Last Decade
Michaela Götz
9 papers receiving 385 citations
Peers
Comparison fields: 5 of 31
- Computer Science Applications 69
- Management Science and Operations Research 137
- Artificial Intelligence 332
- Transportation 29
- Information Systems 89
Countries citing papers authored by Michaela Götz
This map shows the geographic impact of Michaela Götz'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 Michaela Götz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michaela Götz more than expected).
Fields of papers citing papers by Michaela Götz
This network shows the impact of papers produced by Michaela Götz. 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 Michaela Götz. The network helps show where Michaela Götz may publish in the future.
Co-authors
The 9 scholars most cited alongside Michaela Götz, 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 | 2010 | 144 | |
| 2 | 2012 | 85 | |
| 3 | 2011 | 83 | |
| 4 | 2009 | 39 | |
| 5 | 2009 | 12 | |
| 6 | 2009 | 11 | |
| 7 | 2010 | 11 | |
| 8 | 2007 | 10 | |
| 9 | 2011 | 4 |
About Michaela Götz
Michaela Götz is a scholar working on Artificial Intelligence, Computer Networks and Communications, Signal Processing, Computer Science Applications and Sociology and Political Science, having authored 9 papers that have together received 399 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (4 papers), Advanced Database Systems and Queries (4 papers), Data Management and Algorithms (3 papers), Privacy, Security, and Data Protection (3 papers), Algorithms and Data Compression (3 papers), Peer-to-Peer Network Technologies (2 papers), Internet Traffic Analysis and Secure E-voting (1 paper) and Cryptography and Data Security (1 paper). The work is most often cited by research in Computer Science Applications (69 citations), Management Science and Operations Research (137 citations), Artificial Intelligence (332 citations), Transportation (29 citations) and Information Systems (89 citations). Michaela Götz has collaborated with scholars based in United States, Norway and Germany. Frequent co-authors include Johannes E. Gehrke, Raghav Kaushik, Arvind Arasu, Ashwin Machanavajjhala, Suman Nath, Xiaokui Xiao, Guozhang Wang, Christoph E. Koch and Wim Martens. Their work appears in journals such as Information Systems, IEEE Transactions on Knowledge and Data Engineering, Proceedings of the VLDB Endowment, Lecture notes in computer science and International Conference on Management of Data.
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.