Daniel Puschmann
Impact in
- Transportation top 5%
- Human Mobility and Location-Based Analysis
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- IoT and Edge/Fog Computing
Papers in
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- Time Series Analysis and Forecasting 7
- Data Management and Algorithms 3
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- Data Stream Mining Techniques 4
- Anomaly Detection Techniques and Applications 3
- Co-authors
- Payam Barnaghi (8 shared papers)Frieder Ganz (2 shared papers)Şefki Kolozali (3 shared papers)Rahim Tafazolli (3 shared papers)M. Bermúdez-Edo (2 shared papers)François Carrez (1 shared paper)Cosmin-Septimiu Nechifor (1 shared paper)Feng Gao (1 shared paper)
- Journals
- IEEE Internet of Things Journal (3 papers)IEEE Systems Journal (1 paper)PLoS ONE (1 paper)IEEE Access (1 paper)Open Access at Essex (University of Essex) (1 paper)
- Partner nations
- United KingdomIrelandUnited States
In The Last Decade
Daniel Puschmann
8 papers receiving 559 citations
Peers
Comparison fields: 5 of 81
- Transportation 92
- Computer Networks and Communications 265
- Signal Processing 115
- Media Technology 70
- Artificial Intelligence 217
Countries citing papers authored by Daniel Puschmann
This map shows the geographic impact of Daniel Puschmann'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 Daniel Puschmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Puschmann more than expected).
Fields of papers citing papers by Daniel Puschmann
This network shows the impact of papers produced by Daniel Puschmann. 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 Daniel Puschmann. The network helps show where Daniel Puschmann may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Puschmann, 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 | 2016 | 195 | |
| 2 | 2015 | 105 | |
| 3 | 2016 | 103 | |
| 4 | 2014 | 96 | |
| 5 | 2016 | 35 | |
| 6 | 2018 | 22 | |
| 7 | 2017 | 16 | |
| 8 | 2016 | 2 | |
| 9 | 2005 | 0 |
About Daniel Puschmann
Daniel Puschmann is a scholar working on Signal Processing, Artificial Intelligence, Experimental and Cognitive Psychology, Building and Construction and Social Psychology, having authored 9 papers that have together received 574 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (7 papers), Data Stream Mining Techniques (4 papers), Anomaly Detection Techniques and Applications (3 papers), Data Management and Algorithms (3 papers), 3D IC and TSV technologies (1 paper), Mental Health Research Topics (1 paper), Mental Health via Writing (1 paper) and Cancer survivorship and care (1 paper). The work is most often cited by research in Transportation (92 citations), Computer Networks and Communications (265 citations), Signal Processing (115 citations), Media Technology (70 citations) and Artificial Intelligence (217 citations). Daniel Puschmann has collaborated with scholars based in United Kingdom, Ireland and United States. Frequent co-authors include Payam Barnaghi, Frieder Ganz, Şefki Kolozali, Rahim Tafazolli, M. Bermúdez-Edo, François Carrez, Cosmin-Septimiu Nechifor, Feng Gao, João F. P. Fernandes and Marten Fischer. Their work appears in journals such as IEEE Internet of Things Journal, IEEE Systems Journal, PLoS ONE, IEEE Access and Open Access at Essex (University of Essex).
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.