What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- The Internet
His primary scientific interests are in Wireless sensor network, Distributed computing, Scalability, Peer-to-peer and Computer network.
As part of one scientific family, Vana Kalogeraki deals mainly with the area of Wireless sensor network, narrowing it down to issues related to the Data mining, and often Outlier and Flooding.
The Distributed computing study combines topics in areas such as Python, Key distribution in wireless sensor networks and Software development.
His Scalability study incorporates themes from Distributed algorithm, Local search, Protocol and Search engine.
His biological study spans a wide range of topics, including Middleware, Bandwidth, Information retrieval and Dissemination.
While the research belongs to areas of Stream processing, Vana Kalogeraki spends his time largely on the problem of Resource allocation, intersecting his research to questions surrounding Quality of service and Resource allocation.
His most cited work include:
- Peer-to-Peer Computing (769 citations)
- A local search mechanism for peer-to-peer networks (371 citations)
- Online outlier detection in sensor data using non-parametric models (339 citations)
What are the main themes of his work throughout his whole career to date?
Vana Kalogeraki spends much of his time researching Distributed computing, Computer network, Scalability, Wireless sensor network and Real-time computing.
His Distributed computing research includes themes of Quality of service, Scheduling and Big data.
In Computer network, Vana Kalogeraki works on issues like Overlay network, which are connected to Network topology.
His study in Scalability is interdisciplinary in nature, drawing from both Overlay, Replication, Consistency and Software deployment.
His Wireless sensor network study integrates concerns from other disciplines, such as Network congestion, Data management, Data mining, Key distribution in wireless sensor networks and Node.
As a member of one scientific family, he mostly works in the field of Real-time computing, focusing on Distributed algorithm and, on occasion, Resource allocation.
He most often published in these fields:
- Distributed computing (38.29%)
- Computer network (20.27%)
- Scalability (20.27%)
What were the highlights of his more recent work (between 2014-2020)?
- Distributed computing (38.29%)
- Big data (7.21%)
- Event (8.56%)
In recent papers he was focusing on the following fields of study:
Distributed computing, Big data, Event, Social network and Real-time computing are his primary areas of study.
He has researched Distributed computing in several fields, including Scheduling, Cloud computing and Cache.
The various areas that he examines in his Social network study include Maximization, Data science and Behavioral pattern.
His work focuses on many connections between Data science and other disciplines, such as Data management, that overlap with his field of interest in Wireless sensor network.
Many of his studies on Stream processing involve topics that are commonly interrelated, such as Scalability.
His Scalability study combines topics in areas such as Complex event processing and Data stream mining.
Between 2014 and 2020, his most popular works were:
- Elastic complex event processing exploiting prediction (38 citations)
- Intelligent Urban Data Monitoring for Smart Cities (25 citations)
- Insights on a Scalable and Dynamic Traffic Management System (22 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- The Internet
His primary areas of investigation include Event, Complex event processing, Distributed computing, Social network and Scalability.
His Event research is multidisciplinary, incorporating perspectives in Data mining, Reliability and Transport engineering.
His studies deal with areas such as Consistency, Optimization problem, Wireless sensor network and Service as well as Data mining.
His Distributed computing study deals with Scheduling intersecting with Power consumption and Batch processing.
In his study, Middleware is inextricably linked to Behavioral pattern, which falls within the broad field of Social network.
His Scalability course of study focuses on Real-time computing and Stream processing, Cloud computing and Data stream mining.
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