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 Computer network, Bandwidth, The Internet, Network packet and Packet switching.
His work on Router as part of general Computer network research is often related to Sizing, thus linking different fields of science.
His work deals with themes such as Instability, Bottleneck and Focus, which intersect with Bandwidth.
The study incorporates disciplines such as Distributed computing and Server in addition to The Internet.
His Network packet study often links to related topics such as Throughput.
His research in Packet switching intersects with topics in Quality of service and Queueing theory.
His most cited work include:
- Bandwidth estimation: metrics, measurement techniques, and tools (702 citations)
- An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP (617 citations)
- What do packet dispersion techniques measure (535 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computer network, The Internet, Distributed computing, Computer security and Peering.
His work investigates the relationship between Computer network and topics such as Multihoming that intersect with problems in Overlay network.
His The Internet study is focused on World Wide Web in general.
His research investigates the connection between Distributed computing and topics such as Network topology that intersect with issues in Complex network.
His Network packet study combines topics in areas such as Bandwidth, Queueing theory, Real-time computing and Throughput.
His Bandwidth research incorporates elements of Bottleneck and Focus.
He most often published in these fields:
- Computer network (39.37%)
- The Internet (34.65%)
- Distributed computing (15.75%)
What were the highlights of his more recent work (between 2016-2021)?
- Hourglass (7.87%)
- Artificial intelligence (9.45%)
- Theoretical computer science (7.09%)
In recent papers he was focusing on the following fields of study:
Constantine Dovrolis mainly investigates Hourglass, Artificial intelligence, Theoretical computer science, Modularity and Cluster analysis.
The various areas that Constantine Dovrolis examines in his Artificial intelligence study include Multisensory integration and Pattern recognition.
As a part of the same scientific family, Constantine Dovrolis mostly works in the field of Theoretical computer science, focusing on Complex system and, on occasion, Function and Core.
His Hierarchical clustering study in the realm of Cluster analysis interacts with subjects such as Default mode network.
His Artificial neural network research integrates issues from Algorithm and Ticket.
Constantine Dovrolis has included themes like Fuzzy clustering, Consensus clustering, Traffic engineering, Correlation clustering and Hierarchical network model in his Data mining study.
Between 2016 and 2021, his most popular works were:
- The hourglass effect in hierarchical dependency networks (12 citations)
- Advancing climate science with knowledge-discovery through data mining (10 citations)
- δ -MAPS: from spatio-temporal data to a weighted and lagged network between functional domains (8 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Artificial intelligence
Constantine Dovrolis focuses on Cluster analysis, Function, Hourglass, Artificial intelligence and Unsupervised learning.
His Cluster analysis study incorporates themes from Function and Resting state fMRI.
His study in Function is interdisciplinary in nature, drawing from both Structure, Complex system and Network science.
His Hourglass investigation overlaps with other areas such as Artificial neural network, Sensory system, Information processing, Information bottleneck method and Neuroscience.
His study in the field of Content-addressable memory and Feature learning is also linked to topics like Novelty detection and Episodic memory.
Constantine Dovrolis interconnects NetFlow, CURE data clustering algorithm, Canopy clustering algorithm, Data mining and Flow network in the investigation of issues within Unsupervised learning.
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