Jinjun Chen mostly deals with Cloud computing, Big data, Data mining, Distributed computing and Scalability. Jinjun Chen interconnects Computer security, Volume and Workflow, Database in the investigation of issues within Cloud computing. His Workflow research is multidisciplinary, incorporating perspectives in Dependency and Scheduling.
His work in Big data addresses subjects such as Data security, which are connected to disciplines such as Data integrity, Block and Dynamic data. Jinjun Chen has researched Distributed computing in several fields, including Redundancy, Denial-of-service attack, Network security and Network packet. His study in Scalability is interdisciplinary in nature, drawing from both Distributed database, Collaborative filtering, Data anonymization, World Wide Web and Information management.
Jinjun Chen spends much of his time researching Cloud computing, Distributed computing, Workflow, Big data and Data mining. His research integrates issues of Scalability, Database and Computer security, Information privacy, Encryption in his study of Cloud computing. His Distributed computing research also works with subjects such as
The various areas that Jinjun Chen examines in his Workflow study include Virtual machine and Business process. Jinjun Chen focuses mostly in the field of Big data, narrowing it down to matters related to Data security and, in some cases, Cloud computing security. His biological study spans a wide range of topics, including Workflow technology and Workflow engine.
Jinjun Chen focuses on Blockchain, Algorithm, Cloud computing, Differential privacy and Computer security. The concepts of his Blockchain study are interwoven with issues in Node, Computer network and Hash function. He combines subjects such as Distributed computing, Encryption, Computational science and Big data with his study of Cloud computing.
His biological study spans a wide range of topics, including Scheduling, Efficient energy use and Scalability. Jinjun Chen studied Scheduling and Workflow that intersect with Private information retrieval and Optimization problem. He works mostly in the field of Computer security, limiting it down to topics relating to Cyber-physical system and, in certain cases, Data processing, Social system, Human–computer interaction, B-tree and Floorplan, as a part of the same area of interest.
Jinjun Chen mainly investigates Algorithm, Cloud computing, Private information retrieval, Distributed computing and Scheduling. His studies deal with areas such as Chaotic, Computational intelligence and Hop as well as Algorithm. He has included themes like Image, Encryption, Random permutation and Compressed sensing in his Cloud computing study.
His research on Distributed computing frequently connects to adjacent areas such as Workflow. His Workflow study combines topics from a wide range of disciplines, such as Optimization problem and Big data. In general Computer security, his work in Information privacy is often linked to Medical systems linking many areas of study.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A data placement strategy in scientific cloud workflows
Dong Yuan;Yun Yang;Xiao Liu;Jinjun Chen.
Future Generation Computer Systems (2010)
A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloud
Xuyun Zhang;Laurence T. Yang;Chang Liu;Jinjun Chen.
IEEE Transactions on Parallel and Distributed Systems (2014)
A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyberphysical systems
Zhihua Cui;Bin Sun;Gaige Wang;Yu Xue.
Journal of Parallel and Distributed Computing (2017)
Detection of Malicious Code Variants Based on Deep Learning
Zhihua Cui;Fei Xue;Xingjuan Cai;Yang Cao.
IEEE Transactions on Industrial Informatics (2018)
Authorized Public Auditing of Dynamic Big Data Storage on Cloud with Efficient Verifiable Fine-Grained Updates
Chang Liu;Jinjun Chen;Laurence T. Yang;Xuyun Zhang.
IEEE Transactions on Parallel and Distributed Systems (2014)
A Compromised-Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost-Constrained Workflows on a Cloud Computing Platform
Ke Liu;Hai Jin;Jinjun Chen;Xiao Liu.
ieee international conference on high performance computing data and analytics (2010)
KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications
Shunmei Meng;Wanchun Dou;Xuyun Zhang;Jinjun Chen.
IEEE Transactions on Parallel and Distributed Systems (2014)
Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things
Zhihua Cui;Yang Cao;Xingjuan Cai;Jianghui Cai.
Journal of Parallel and Distributed Computing (2018)
Hybrid multi-objective cuckoo search with dynamical local search
Maoqing Zhang;Hui Wang;Zhihua Cui;Jinjun Chen;Jinjun Chen.
Memetic Computing (2018)
A Privacy Leakage Upper Bound Constraint-Based Approach for Cost-Effective Privacy Preserving of Intermediate Data Sets in Cloud
Xuyun Zhang;Chang Liu;Surya Nepal;Suraj Pandey.
IEEE Transactions on Parallel and Distributed Systems (2013)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
Swinburne University of Technology
Macquarie University
Nanjing University
Newcastle University
Commonwealth Scientific and Industrial Research Organisation
Taiyuan University of Science and Technology
St. Francis Xavier University
Cork Institute of Technology
Chinese Academy of Sciences
China University of Geosciences
Stanford University
University of Illinois at Urbana-Champaign
Chinese Academy of Sciences
Syracuse University
University of Minnesota
The University of Texas at Austin
Kwansei Gakuin University
Huazhong University of Science and Technology
Durham University
Smithsonian Conservation Biology Institute
University of Queensland
University of Colorado Boulder
St. Michael's GAA, Sligo
Peter MacCallum Cancer Centre
University of California, San Diego