What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Software
His scientific interests lie mostly in Distributed computing, Scheduling, Grid computing, Nimrod and Resource management.
His study in Distributed computing is interdisciplinary in nature, drawing from both Software architecture, The Internet, Dynamic priority scheduling and Dataflow.
David Abramson interconnects Transportation theory, Project management, Reliability and Operations research in the investigation of issues within Scheduling.
The concepts of his Grid computing study are interwoven with issues in Virtual Laboratory, Semantic grid, Specification language and Usability.
His Semantic grid research focuses on Utility computing and how it relates to Shared resource.
His Resource management research is multidisciplinary, incorporating elements of Testbed and Resource allocation.
His most cited work include:
- Nimrod/G: an architecture for a resource management and scheduling system in a global computational grid (863 citations)
- Economic models for resource management and scheduling in Grid computing (857 citations)
- The Grid Economy (457 citations)
What are the main themes of his work throughout his whole career to date?
David Abramson focuses on Distributed computing, Grid computing, Debugging, Workflow and Nimrod.
David Abramson has researched Distributed computing in several fields, including Supercomputer, Software, Application software, Testbed and Scheduling.
His work carried out in the field of Grid computing brings together such families of science as Middleware, Semantic grid and Resource management.
David Abramson regularly links together related areas like Resource allocation in his Resource management studies.
His studies deal with areas such as Data structure and Porting as well as Debugging.
He merges Nimrod with Computational science in his study.
He most often published in these fields:
- Distributed computing (27.56%)
- Grid computing (24.36%)
- Debugging (13.14%)
What were the highlights of his more recent work (between 2012-2021)?
- Supercomputer (8.33%)
- Workflow (12.50%)
- Debugging (13.14%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Supercomputer, Workflow, Debugging, Parallel computing and Distributed computing.
In the subject of general Workflow, his work in Workflow engine, Workflow management system and Workflow technology is often linked to Science and engineering, thereby combining diverse domains of study.
His Workflow engine research incorporates themes from Python, Resource allocation, Scheduling, Computational steering and Server.
His biological study spans a wide range of topics, including Data structure and Porting.
His Parallel computing study combines topics from a wide range of disciplines, such as Software and Numerical analysis.
His Distributed computing study frequently links to adjacent areas such as Cloud computing.
Between 2012 and 2021, his most popular works were:
- Fire in Australian savannas: From leaf to landscape (62 citations)
- Targeting EphA3 inhibits cancer growth by disrupting the tumor stromal microenvironment. (49 citations)
- New insights into the analysis of the electrode kinetics of flavin adenine dinucleotide redox center of glucose oxidase immobilized on carbon electrodes (21 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Software
His primary areas of investigation include Workflow, Supercomputer, Parallel computing, Data science and Computational science.
His Workflow research includes themes of e-Science and Distributed computing.
His work often combines Distributed computing and Nimrod studies.
He interconnects Software and Efficient energy use in the investigation of issues within Supercomputer.
His work deals with themes such as Debugging, Algorithmic program debugging and Porting, which intersect with Parallel computing.
He has researched Computational science in several fields, including Rainforest, Event, Great barrier reef and Big data.
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