Philip Empl
Impact in
-
- Digital Transformation in Industry
- Information Systems top 5%
- Blockchain Technology Applications and Security
- Information and Cyber Security
- Internet of Things and AI
Papers in
-
- Network Security and Intrusion Detection 6
- Advanced Database Systems and Queries 1
-
- Information and Cyber Security 3
- Internet of Things and AI 2
- Co-authors
- Günther Pernul (9 shared papers)Marietheres Dietz (2 shared papers)Benedikt Putz (1 shared paper)Stefan Schönig (2 shared papers)Thomas Schreck (1 shared paper)Marco Caselli (1 shared paper)Gerit Wagner (1 shared paper)Fabian Böhm (1 shared paper)
In The Last Decade
Philip Empl
12 papers receiving 293 citations
Peers
Comparison fields: 5 of 37
- Industrial and Manufacturing Engineering 84
- Information Systems 180
- Computer Networks and Communications 79
- Building and Construction 16
- Artificial Intelligence 29
Countries citing papers authored by Philip Empl
This map shows the geographic impact of Philip Empl'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 Philip Empl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip Empl more than expected).
Fields of papers citing papers by Philip Empl
This network shows the impact of papers produced by Philip Empl. 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 Philip Empl. The network helps show where Philip Empl may publish in the future.
Co-authors
The 9 scholars most cited alongside Philip Empl, 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 | 2020 | 188 | |
| 2 | 2023 | 33 | |
| 3 | 2021 | 19 | |
| 4 | 2022 | 19 | |
| 5 | 2024 | 12 | |
| 6 | 2024 | 10 | |
| 7 | 2023 | 9 | |
| 8 | 2023 | 7 | |
| 9 | 2024 | 3 | |
| 10 | 2025 | 3 | |
| 11 | 2020 | 2 | |
| 12 | 2024 | 2 |
About Philip Empl
Philip Empl is a scholar working on Computer Networks and Communications, Information Systems, Industrial and Manufacturing Engineering, Statistics, Probability and Uncertainty and Management Information Systems, having authored 12 papers that have together received 307 indexed citations. Recurring topics across this work include Network Security and Intrusion Detection (6 papers), Information and Cyber Security (3 papers), Digital Transformation in Industry (2 papers), Internet of Things and AI (2 papers), scientometrics and bibliometrics research (1 paper), Data Visualization and Analytics (1 paper), Business Process Modeling and Analysis (1 paper) and Advanced Database Systems and Queries (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (84 citations), Information Systems (180 citations), Computer Networks and Communications (79 citations), Building and Construction (16 citations) and Artificial Intelligence (29 citations). Philip Empl has collaborated with scholars based in Germany and Canada. Frequent co-authors include Günther Pernul, Marietheres Dietz, Benedikt Putz, Stefan Schönig, Thomas Schreck, Marco Caselli, Gerit Wagner, Fabian Böhm and Guido Schryen. Their work appears in journals such as Future Internet, Information Processing & Management, ACM Computing Surveys, Sensors and International Journal of Information Security.
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.