Changyeon Jo

402 total citations
12 papers, 288 citations indexed

About

Changyeon Jo is a scholar working on Computer Networks and Communications, Information Systems and Hardware and Architecture. According to data from OpenAlex, Changyeon Jo has authored 12 papers receiving a total of 288 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Networks and Communications, 7 papers in Information Systems and 4 papers in Hardware and Architecture. Recurrent topics in Changyeon Jo's work include Cloud Computing and Resource Management (7 papers), Advanced Data Storage Technologies (7 papers) and Caching and Content Delivery (3 papers). Changyeon Jo is often cited by papers focused on Cloud Computing and Resource Management (7 papers), Advanced Data Storage Technologies (7 papers) and Caching and Content Delivery (3 papers). Changyeon Jo collaborates with scholars based in South Korea, Switzerland and United States. Changyeon Jo's co-authors include Bernhard Egger, Ki‐Chang Nam, Jong‐Soo Chang, Erik Gustafsson, Bokyung Lee, Aera Jang, Soo Kee Lee, Jaejin Lee, Younghyun Cho and Thomas R. Gross and has published in prestigious journals such as IEEE Transactions on Computers, Osteoarthritis and Cartilage and ACM SIGPLAN Notices.

In The Last Decade

Changyeon Jo

12 papers receiving 276 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Changyeon Jo South Korea	7	155	139	80	48	38	12	288
Mingjie Xing China	8	123 0.8×	37 0.3×	67 0.8×	7 0.1×	18 0.5×	15	335
J. Klensin United States	6	94 0.6×	65 0.5×	5 0.1×	36 0.8×	28 0.7×	22	296
Sang-Hoon Lee South Korea	8	61 0.4×	94 0.7×	13 0.2×	5 0.1×	43 1.1×	33	315
Yoshifumi Sakai Japan	8	43 0.3×	54 0.4×	5 0.1×	13 0.3×	6 0.2×	38	195
S.M. Bridges United States	10	126 0.8×	42 0.3×	6 0.1×	18 0.4×	14 0.4×	14	342
Lizhong Xiao China	9	47 0.3×	32 0.2×	23 0.3×	4 0.1×	35 0.9×	25	350
Chowdhury Canada	9	27 0.2×	16 0.1×	107 1.3×	2 0.0×	39 1.0×	25	354
Chihong Zhang China	11	32 0.2×	12 0.1×	7 0.1×	44 0.9×	7 0.2×	20	414
Olegario Hernández Argentina	4	92 0.6×	28 0.2×	17 0.2×	7 0.1×	9 0.2×	9	142
Xiaoyang Zhu China	7	69 0.4×	92 0.7×	3 0.0×	4 0.1×	13 0.3×	10	250

Countries citing papers authored by Changyeon Jo

Since Specialization

Citations

This map shows the geographic impact of Changyeon Jo'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 Changyeon Jo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Changyeon Jo more than expected).

Fields of papers citing papers by Changyeon Jo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Changyeon Jo. 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 Changyeon Jo. The network helps show where Changyeon Jo may publish in the future.

Co-authorship network of co-authors of Changyeon Jo

This figure shows the co-authorship network connecting the top 25 collaborators of Changyeon Jo. A scholar is included among the top collaborators of Changyeon Jo 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 Changyeon Jo. Changyeon Jo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Cho, Younghyun, et al.. (2022). Dopia. 32–45. 4 indexed citations

2.

Jo, Changyeon, et al.. (2021). Reliability of landmark recognition and angle measurement of lower extremity X-ray using deep learning algorithm. Osteoarthritis and Cartilage. 29. S340–S341. 1 indexed citations

3.

Jo, Changyeon, et al.. (2020). RackMem. 467–480. 4 indexed citations

4.

Jo, Changyeon, et al.. (2017). A machine learning approach to live migration modeling. 351–364. 44 indexed citations

5.

Egger, Bernhard, et al.. (2016). Efficient Checkpointing of Live Virtual Machines. IEEE Transactions on Computers. 65(10). 3041–3054. 8 indexed citations

6.

Jo, Changyeon, et al.. (2013). Efficient live migration of virtual machines using shared storage. ACM SIGPLAN Notices. 48(7). 41–50. 22 indexed citations

7.

Egger, Bernhard, et al.. (2013). Efficiently Restoring Virtual Machines. International Journal of Parallel Programming. 43(3). 421–439. 6 indexed citations

8.

Jo, Changyeon, et al.. (2013). Efficient live migration of virtual machines using shared storage. 41–50. 70 indexed citations

9.

Jo, Changyeon & Bernhard Egger. (2013). Optimizing Live Migration for Virtual Desktop Clouds. 104–111. 5 indexed citations

10.

Jo, Changyeon, et al.. (2012). Keys to production and processing of Hanwoo beef: A perspective of tradition and science. Animal Frontiers. 2(4). 32–38. 104 indexed citations

11.

Jang, Aera, et al.. (2009). Effect of Dietary Inclusion of Medicinal Herb Extract Mix in a Poultry Ration on the Physico-chemical Quality and Oxidative Stability of Eggs. Asian-Australasian Journal of Animal Sciences. 22(3). 421–427. 15 indexed citations

12.

Jung, Samooel, et al.. (2008). Effect of Dietary Medicinal Herb Extract Mix and Antibiotics (Albac G150) on the Oxidative Stability of Chicken Meat. Korean Journal of Poultry Science. 35(1). 29–37. 5 indexed citations

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.

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