What is he best known for?
The fields of study he is best known for:
- The Internet
- Operating system
- Computer network
Jeff Burke mostly deals with Participatory sensing, World Wide Web, Mobile phone, Human–computer interaction and Content based networking.
His studies deal with areas such as Data stream mining, Privacy policy, Usability, Data custodian and Information sensitivity as well as World Wide Web.
The concepts of his Mobile phone study are interwoven with issues in Decision tree, Upload, Hidden Markov model and Accelerometer.
Jeff Burke has researched Human–computer interaction in several fields, including User interface, Software system and Embedded system.
His Content based networking research is multidisciplinary, incorporating perspectives in Network architecture, Content centric, Content centric networking and Information-centric networking.
He undertakes multidisciplinary studies into Network architecture and Implementation in his work.
His most cited work include:
- Named data networking (1380 citations)
- Named Data Networking (NDN) Project (819 citations)
- Using mobile phones to determine transportation modes (646 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns World Wide Web, Computer network, Participatory sensing, Computer security and Content based networking.
Jeff Burke interconnects Control and Network packet in the investigation of issues within World Wide Web.
His Computer security research includes elements of Data exchange, The Internet, Content centric networking and Host.
His Content based networking research incorporates themes from Routing and Content centric.
His Wireless sensor network research also works with subjects such as
- Embedded system which connect with Focus,
- Ubiquitous computing, which have a strong connection to Multimedia.
His Human–computer interaction study incorporates themes from User interface and Tracking data.
He most often published in these fields:
- World Wide Web (22.12%)
- Computer network (20.35%)
- Participatory sensing (18.58%)
What were the highlights of his more recent work (between 2016-2021)?
- Computer network (20.35%)
- Content based networking (15.04%)
- Multimedia (15.04%)
In recent papers he was focusing on the following fields of study:
Computer network, Content based networking, Multimedia, Network architecture and Synchronization are his primary areas of study.
His Content based networking research integrates issues from Device to device, Phase and Documentation.
His Multimedia research incorporates elements of Protocol stack, World Wide Web, Virtual reality and Control.
His research in World Wide Web intersects with topics in Namespace, Network packet and Resilience.
Network architecture is a primary field of his research addressed under Computer security.
In his study, Jeff Burke carries out multidisciplinary Synchronization and Implementation research.
Between 2016 and 2021, his most popular works were:
- A Brief Introduction to Named Data Networking (36 citations)
- Towards Edge Computing over Named Data Networking (24 citations)
- Breaking out of the Cloud: Local Trust Management and Rendezvous in Named Data Networking of Things (16 citations)
In his most recent research, the most cited papers focused on:
- The Internet
- Operating system
- Computer network
The scientist’s investigation covers issues in Computer network, Network architecture, Augmented reality, Synchronization and Multicast.
His study in Home automation extends to Computer network with its themes.
His Network architecture study integrates concerns from other disciplines, such as Software architecture and The Internet.
His studies in Augmented reality integrate themes in fields like Network packet, Resilience, Multimedia, World Wide Web and Virtual reality.
His Synchronization study combines topics in areas such as Interoperation, Trust management, Internet Protocol and Cloud computing.
He has included themes like Content based networking and Subject in his Multicast 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.
