What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Computer network
Xuyun Zhang mainly investigates Big data, Scalability, Energy consumption, Distributed computing and Database.
The concepts of his Big data study are interwoven with issues in Computer security, Data security and Server.
His Scalability research is multidisciplinary, relying on both Information management and World Wide Web.
His Energy consumption research is multidisciplinary, incorporating perspectives in Edge computing and Computation offloading.
His Distributed computing study deals with Resource allocation intersecting with Leverage, Workflow management system and Workflow.
As a part of the same scientific study, Xuyun Zhang usually deals with the Distributed database, concentrating on Information privacy and frequently concerns with Data mining and Encryption.
His most cited work include:
- A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloud (177 citations)
- Authorized Public Auditing of Dynamic Big Data Storage on Cloud with Efficient Verifiable Fine-Grained Updates (145 citations)
- A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data (128 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Big data, Distributed computing, Scalability, Data mining and Computer network.
His Big data research incorporates themes from Computer security, Data security, Analytics and Information management.
He works mostly in the field of Distributed computing, limiting it down to topics relating to Energy consumption and, in certain cases, Server.
His work deals with themes such as Data anonymization and Information privacy, which intersect with Scalability.
Many of his research projects under Data mining are closely connected to Data stream with Data stream, tying the diverse disciplines of science together.
His work is dedicated to discovering how Computer network, Edge computing are connected with The Internet, Mobile device and Mobile edge computing and other disciplines.
He most often published in these fields:
- Big data (27.78%)
- Distributed computing (19.44%)
- Scalability (17.36%)
What were the highlights of his more recent work (between 2019-2021)?
- Edge computing (11.81%)
- Computer network (13.89%)
- Quality of service (10.42%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Edge computing, Computer network, Quality of service, Distributed computing and Recommender system.
His research integrates issues of Energy consumption, Mobile edge computing and Resource allocation in his study of Edge computing.
His Computer network study incorporates themes from The Internet and Edge server.
In the field of Distributed computing, his study on Load balancing overlaps with subjects such as Pareto principle.
His study in Recommender system is interdisciplinary in nature, drawing from both Differential privacy and Big data.
His study looks at the relationship between Big data and topics such as Data analysis, which overlap with Scalability.
Between 2019 and 2021, his most popular works were:
- A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems (112 citations)
- BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing (103 citations)
- A Game-Theoretical Approach for User Allocation in Edge Computing Environment (65 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Computer network
His main research concerns Quality of service, Collaborative filtering, Edge computing, Distributed computing and Energy consumption.
His research in Edge computing intersects with topics in Evolutionary algorithm, Load balancing and Service placement.
He interconnects Energy conservation, Efficient energy use and Job shop scheduling in the investigation of issues within Distributed computing.
His Recommender system study integrates concerns from other disciplines, such as Scalability, Usage data, Service and Big data.
His research in Scalability tackles topics such as Key which are related to areas like Information privacy and Smart city.
The Enhanced Data Rates for GSM Evolution study combines topics in areas such as Computer network and Data mining.
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