What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Artificial intelligence
His main research concerns Mobile device, Distributed computing, Edge computing, Scalability and Computation offloading.
His research investigates the connection between Mobile device and topics such as Mobile service that intersect with problems in Dimension, Enhanced Data Rates for GSM Evolution, Computer security and Mobile computing.
His Distributed computing research incorporates themes from Quality of service and Job shop scheduling.
His Scalability research is multidisciplinary, incorporating elements of Collaborative filtering and World Wide Web.
Lianyong Qi combines subjects such as Services computing and Locality-sensitive hashing with his study of Collaborative filtering.
His studies in Computation offloading integrate themes in fields like Genetic algorithm, Computer network and The Internet.
His most cited work include:
- A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data (128 citations)
- A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment (116 citations)
- A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment (116 citations)
What are the main themes of his work throughout his whole career to date?
Lianyong Qi mainly investigates Edge computing, Collaborative filtering, Distributed computing, Web service and World Wide Web.
His studies examine the connections between Edge computing and genetics, as well as such issues in Computer network, with regards to Mobile device.
His work deals with themes such as Mobile service and Mobile computing, which intersect with Mobile device.
His Collaborative filtering study integrates concerns from other disciplines, such as Locality-sensitive hashing and Service.
His Distributed computing study deals with Cloudlet intersecting with Mobile cloud computing.
His study looks at the relationship between Web service and topics such as Quality of service, which overlap with Database and Data mining.
He most often published in these fields:
- Edge computing (21.51%)
- Collaborative filtering (26.88%)
- Distributed computing (16.67%)
What were the highlights of his more recent work (between 2019-2021)?
- Edge computing (21.51%)
- Enhanced Data Rates for GSM Evolution (15.59%)
- Computer security (16.13%)
In recent papers he was focusing on the following fields of study:
Edge computing, Enhanced Data Rates for GSM Evolution, Computer security, Computer network and Recommender system are his primary areas of study.
His Edge computing research includes themes of Distributed computing, Resource allocation, The Internet and Server.
In his study, which falls under the umbrella issue of Distributed computing, Cloudlet and Mobile device is strongly linked to Scheduling.
His work in the fields of Collaborative filtering overlaps with other areas such as Popularity.
He interconnects Locality-sensitive hashing and Artificial intelligence in the investigation of issues within Collaborative filtering.
His study in Locality-sensitive hashing is interdisciplinary in nature, drawing from both Quality of service and Web service.
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)
- Joint Optimization of Offloading Utility and Privacy for Edge Computing Enabled IoT (70 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Artificial intelligence
The scientist’s investigation covers issues in Quality of service, Edge computing, Enhanced Data Rates for GSM Evolution, Collaborative filtering and Recommender system.
His work carried out in the field of Quality of service brings together such families of science as Database, Service quality, Cloud testing and Scheduling, Job shop scheduling.
His Edge computing research integrates issues from Distributed computing, Evolutionary algorithm, The Internet and Server.
His research investigates the link between Enhanced Data Rates for GSM Evolution and topics such as TOPSIS that cross with problems in Data transmission, Multiple-criteria decision analysis, Mobile device and Data mining.
He combines topics linked to Locality-sensitive hashing with his work on Collaborative filtering.
In his work, Data-driven is strongly intertwined with World Wide Web, which is a subfield of Locality-sensitive hashing.
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