Workflows and e-Science: An overview of workflow system features and capabilities
By Ewa Deelman, Dennis Gannon, Matthew S. Shields, Ian J. Taylor. Future generations computer systems, 2009
961 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/af0bc4baa5a107964f489298efa34114aacf3b9e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Ewa Deelman
- Karan Vahi
- Gideon Juve
- Esther Pacitti
- Marta Mattoso
- Ji Liu
- Patrick Valduriez
- Miron Livny
In Information Systems and Management
- Ewa Deelman
- Gideon Juve
- Patrick Valduriez
- Esther Pacitti
- Marta Mattoso
- Ji Liu
- Karan Vahi
- Miron Livny
Citations in the corpus
- A market-oriented hierarchical scheduling strategy in cloud workflow systems (2011, background): “Moreover, with the increasing demand for process automation in the cloud, especially for large-scale collaborative and distributed e-business and e-science applications [13, 24, 38], the investigation on cloud workflow scheduling strategies is becoming a significant issue not only in the area of clo”
- Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource (2010, methodology): “At the core of this environment is the Kepler scientific workflow system (13, 14) that supports integration of data and automated Figure 2.”
- Pegasus, a workflow management system for science automation (2015, background): “Many different workflow systems have been developed for executing scientific workflows on distributed infrastructures [60, 80].”
- WorkflowSim: A toolkit for simulating scientific workflows in distributed environments (2012, background): “[13] have provided a survey of popular workflow systems for scientific applications and have classified their components into four categories: composition, mapping, execution, and provenance.”
- A Survey of Data-Intensive Scientific Workflow Management (2015, methodology): “The life cycle of a scientific workflow is a description of the state transitions of a scientific workflow from creation to completion [38, 63].”
- A data placement strategy in scientific cloud workflows (2010, neutral): “In many scientific applications, the temporal data are in large volumes [47].”