What is she best known for?
The fields of study she is best known for:
- Operating system
- Artificial intelligence
- Database
Ewa Deelman mainly focuses on Workflow, Workflow management system, Distributed computing, Workflow technology and Data science.
Her Workflow research incorporates elements of Grid computing, Scheduling, Cloud computing and Software engineering.
Her study in Workflow management system is interdisciplinary in nature, drawing from both Executable, Data management, Workflow engine, Computation and Distributed Computing Environment.
Her Workflow engine study deals with Task intersecting with Cluster analysis.
Her work carried out in the field of Distributed computing brings together such families of science as Programming language, Compiler, Scalability and Data grid.
Her Data science research is multidisciplinary, incorporating elements of Transparency and Application software.
Her most cited work include:
- GW170104: Observation of a 50-Solar-Mass Binary Black Hole Coalescence at Redshift 0.2 (1948 citations)
- Pegasus: A framework for mapping complex scientific workflows onto distributed systems (1072 citations)
- Workflows and e-Science: An overview of workflow system features and capabilities (670 citations)
What are the main themes of her work throughout her whole career to date?
Ewa Deelman mainly investigates Workflow, Distributed computing, Workflow management system, Cloud computing and Data science.
She interconnects Grid, Grid computing, Scheduling and Software engineering in the investigation of issues within Workflow.
Her work focuses on many connections between Grid computing and other disciplines, such as Semantic grid, that overlap with her field of interest in Metadata.
Her Distributed computing research integrates issues from Resource allocation, Resource management, Cluster analysis, Resource and Provisioning.
Ewa Deelman has researched Workflow management system in several fields, including Scalability, Data management, Task, Workflow engine and Workflow technology.
Her work on Windows Workflow Foundation and Workflow Management Coalition as part of general Workflow technology study is frequently linked to Event-driven process chain, therefore connecting diverse disciplines of science.
She most often published in these fields:
- Workflow (57.30%)
- Distributed computing (27.53%)
- Workflow management system (26.40%)
What were the highlights of her more recent work (between 2016-2021)?
- Workflow (57.30%)
- Distributed computing (27.53%)
- Workflow management system (26.40%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Workflow, Distributed computing, Workflow management system, Software engineering and Data science.
Her Workflow study integrates concerns from other disciplines, such as Supercomputer, Set, Task, Resource and Cloud computing.
Her work deals with themes such as Scheduling, Provisioning, File system and Resource management, which intersect with Distributed computing.
The various areas that Ewa Deelman examines in her Workflow management system study include Software deployment, Workflow engine, RAID, Software and Checksum.
The concepts of her Software engineering study are interwoven with issues in Term, Container, Feature and Distributed database.
Ewa Deelman has included themes like Domain, Workflow technology, Key and Knowledge sharing in her Data science study.
Between 2016 and 2021, her most popular works were:
- GW170104: Observation of a 50-Solar-Mass Binary Black Hole Coalescence at Redshift 0.2 (1948 citations)
- Genetic analyses of diverse populations improves discovery for complex traits (209 citations)
- The future of scientific workflows (65 citations)
In her most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Software
Workflow, Distributed computing, Workflow management system, Genomics and Genome-wide association study are her primary areas of study.
Her Workflow technology study, which is part of a larger body of work in Workflow, is frequently linked to Automation, bridging the gap between disciplines.
She combines subjects such as Workflow engine and Computational science with her study of Workflow technology.
The Distributed computing study combines topics in areas such as Resource, Bottleneck, File system and Resource management.
Her Workflow management system research incorporates themes from Software and Software engineering.
Her studies examine the connections between Data science and genetics, as well as such issues in Domain, with regards to Key, Panorama, Testbed and Use case.
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