Heejun Yoon
Impact in
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- Advanced Data Storage Technologies
- Distributed and Parallel Computing Systems
- IoT and Edge/Fog Computing
- Caching and Content Delivery
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- Cloud Computing and Resource Management
Papers in
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- Distributed and Parallel Computing Systems 4
- Advanced Data Storage Technologies 4
- Caching and Content Delivery 1
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- Cloud Computing and Resource Management 3
- Co-authors
- S. Y. Noh (3 shared papers)Moon-Hyun Kim (2 shared papers)Ahmad Waqas (1 shared paper)Taehyung Kim (1 shared paper)Chi-Woong Kim (2 shared papers)S. U. Ahn (3 shared papers)L. Betev (2 shared papers)Hyun‐Chang Lim (2 shared papers)
- Journals
- Applied Sciences (2 papers)Clinical Oral Investigations (1 paper)Electronics (1 paper)Machine Vision and Applications (1 paper)The Journal of Supercomputing (2 papers)
- Partner nations
- South KoreaSwitzerlandPakistan
In The Last Decade
Heejun Yoon
11 papers receiving 31 citations
Peers
Comparison fields: 5 of 20
- Computer Networks and Communications 20
- Information Systems 14
- Hardware and Architecture 4
- Management Information Systems 5
- Urology 2
Countries citing papers authored by Heejun Yoon
This map shows the geographic impact of Heejun Yoon'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 Heejun Yoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heejun Yoon more than expected).
Fields of papers citing papers by Heejun Yoon
This network shows the impact of papers produced by Heejun Yoon. 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 Heejun Yoon. The network helps show where Heejun Yoon may publish in the future.
Co-authors
The 15 scholars most cited alongside Heejun Yoon, 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 | 2021 | 11 | |
| 2 | 2021 | 5 | |
| 3 | 2015 | 4 | |
| 4 | 2014 | 3 | |
| 5 | 2015 | 2 | |
| 6 | 2020 | 2 | |
| 7 | 2022 | 1 | |
| 8 | A Parallel Processing System for a High-Speed Printed Document Recognition. | 1996 | 1 |
| 9 | 2023 | 1 | |
| 10 | 2020 | 1 | |
| 11 | 2022 | 1 | |
| 12 | 2023 | 0 |
About Heejun Yoon
Heejun Yoon is a scholar working on Computer Networks and Communications, Information Systems, Computer Vision and Pattern Recognition, Information Systems and Management and Oral Surgery, having authored 12 papers that have together received 32 indexed citations. Recurring topics across this work include Distributed and Parallel Computing Systems (4 papers), Advanced Data Storage Technologies (4 papers), Cloud Computing and Resource Management (3 papers), Scientific Computing and Data Management (3 papers), Digital and Traditional Archives Management (1 paper), Video Surveillance and Tracking Methods (1 paper), Image Processing and 3D Reconstruction (1 paper) and Caching and Content Delivery (1 paper). The work is most often cited by research in Computer Networks and Communications (20 citations), Information Systems (14 citations), Hardware and Architecture (4 citations), Management Information Systems (5 citations) and Urology (2 citations). Heejun Yoon has collaborated with scholars based in South Korea, Switzerland and Pakistan. Frequent co-authors include S. Y. Noh, Moon-Hyun Kim, Ahmad Waqas, Taehyung Kim, Chi-Woong Kim, S. U. Ahn, L. Betev, Hyun‐Chang Lim, B. Panzer-Steindel and Seung‐Yun Shin. Their work appears in journals such as Applied Sciences, Clinical Oral Investigations, Electronics, Machine Vision and Applications and The Journal of Supercomputing.
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.