An efficient astronomical image processing technique using advance dynamic workflow scheduler in cloud environment

Ahmad, Faiyaz; Ahmad, Wakar

doi:10.1007/s41870-022-01027-3

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Of course, these only represent part of the full range of workflows in the natural sciences. It constitutes an exciting follow-up research question: how suitable ChatGPT and other generative LLMs are in other research contexts, such as climate research [ 33 ] and astronomy [ 32 ], and whether it is possible to identify categories or groups of domains that are particularly well (or poorly) supported. Furthermore, we examined only 2 workflow systems, Nextflow and Apache Airflow, leaving other alternatives, such as Snakemake, Taverna, and Pegasus, for future work.…”

Section: Discussionmentioning

confidence: 99%

“…Scientific workflows are widely used by diverse research communities, such as biomedicine [ 31 ], astronomy [ 32 ], climatology [ 33 ], and Earth observation [ 34 ], to manage the dataflow and distributed execution of complex analyses, simulations, and experiments. A scientific workflow comprises a series of interconnected computational steps, often with diverse patterns of dependencies, that define how to process and analyze data to reach a particular research objective.…”

Section: Introductionmentioning

confidence: 99%

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A qualitative assessment of using ChatGPT as large language model for scientific workflow development

Sänger,

De Mecquenem,

Lewińska

et al. 2024

GigaScience

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Background Scientific workflow systems are increasingly popular for expressing and executing complex data analysis pipelines over large datasets, as they offer reproducibility, dependability, and scalability of analyses by automatic parallelization on large compute clusters. However, implementing workflows is difficult due to the involvement of many black-box tools and the deep infrastructure stack necessary for their execution. Simultaneously, user-supporting tools are rare, and the number of available examples is much lower than in classical programming languages. Results To address these challenges, we investigate the efficiency of large language models (LLMs), specifically ChatGPT, to support users when dealing with scientific workflows. We performed 3 user studies in 2 scientific domains to evaluate ChatGPT for comprehending, adapting, and extending workflows. Our results indicate that LLMs efficiently interpret workflows but achieve lower performance for exchanging components or purposeful workflow extensions. We characterize their limitations in these challenging scenarios and suggest future research directions. Conclusions Our results show a high accuracy for comprehending and explaining scientific workflows while achieving a reduced performance for modifying and extending workflow descriptions. These findings clearly illustrate the need for further research in this area.

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Section: Discussionmentioning

confidence: 99%