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Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single-cell transcriptomic data analysis workflow.
30 pages - 2020 IEEE Transactions on Emerging Topics in Computing
FOS: Computer and information sciences, D.1.3, Bioinformatics, D.3.2, D.1.3; D.3.2; C.1.3, Cloud computing, Task analysis, Containers, Tools, Computer architecture, DSL, Pipelines, Workflow, C.1.3, High-Performance Computing, Parallel Computing, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC), Cloud
FOS: Computer and information sciences, D.1.3, Bioinformatics, D.3.2, D.1.3; D.3.2; C.1.3, Cloud computing, Task analysis, Containers, Tools, Computer architecture, DSL, Pipelines, Workflow, C.1.3, High-Performance Computing, Parallel Computing, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC), Cloud
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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