Soon J. Hyun
- Computer Networks and Communications top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Artificial Intelligence
- Signal Processing top 10%
- Co-authors
- Dongman LeeMyungchul KimBen LeeS.Y.W. SuSooyong LeeJunsung LimGonzalo Huerta‐CánepaJaehyoung Lim
- Topics
- Context-Aware Activity Recognition Systems (16 papers)IoT and Edge/Fog Computing (8 papers)Data Management and Algorithms (8 papers)
- Cited by
- Computer Networks and CommunicationsComputer Vision and Pattern RecognitionSignal Processing
- Journals
- IEEE Communications MagazineInformation SciencesIEEE Transactions on Knowledge and Data Engineering
- Partner nations
- South KoreaUnited StatesChina
In The Last Decade
Soon J. Hyun
46 papers receiving 250 citations
Peers
Comparison fields: 5 of 43
- Computer Networks and Communications 168
- Computer Vision and Pattern Recognition 125
- Information Systems 72
- Artificial Intelligence 60
- Signal Processing 48
Countries citing papers authored by Soon J. Hyun
This map shows the geographic impact of Soon J. Hyun'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 Soon J. Hyun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soon J. Hyun more than expected).
Fields of papers citing papers by Soon J. Hyun
This network shows the impact of papers produced by Soon J. Hyun. 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 Soon J. Hyun. The network helps show where Soon J. Hyun may publish in the future.
Co-authorship network of co-authors of Soon J. Hyun
This figure shows the co-authorship network connecting the top 25 collaborators of Soon J. Hyun. A scholar is included among the top collaborators of Soon J. Hyun 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 Soon J. Hyun. Soon J. Hyun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 7 | |
| 3 | 10 | |
| 4 | 13 | |
| 5 | 7 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 1 | |
| 9 | 4 | |
| 10 | 5 | |
| 11 | 13 | |
| 12 | SNQL: a query language for sensor network databases | 5 |
| 13 | 1 | |
| 14 | 10 | |
| 15 | 4 | |
| 16 | ARML: an active rule mark-up language for heterogeneous active information systems. | 11 |
| 17 | 1 | |
| 18 | Extensions to DNS for Supporting Internationalized Domain Names | 1 |
| 19 | 3 | |
| 20 | 1 |
About Soon J. Hyun
Soon J. Hyun is a scholar working on Computer Networks and Communications, Signal Processing and Computer Vision and Pattern Recognition, having authored 49 papers that have together received 279 indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (16 papers), IoT and Edge/Fog Computing (8 papers) and Data Management and Algorithms (8 papers). The work is most often cited by research in Computer Networks and Communications (168 citations), Computer Vision and Pattern Recognition (125 citations) and Signal Processing (48 citations). Soon J. Hyun has collaborated with scholars based in South Korea, United States and China. Frequent co-authors include Dongman Lee, Myungchul Kim, Ben Lee, S.Y.W. Su, Sooyong Lee, Junsung Lim, Gonzalo Huerta‐Cánepa, Jaehyoung Lim, Young‐Kyun Kim and Seunghyun Han. Their work appears in journals such as IEEE Communications Magazine, Information Sciences and IEEE Transactions on Knowledge and Data Engineering.
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.