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Corral Framework: Trustworthy and Fully Functional Data Intensive Parallel Astronomical Pipelines | | Juan B. Cabral
; Bruno Sánchez
; Martín Beroiz
; Mariano Domínguez
; Marcelo Lares
; Sebastián Gurovich
; Pablo Granitto
; | | Date: |
19 Jan 2017 | | Abstract: | Data processing pipelines are one of most common astronomical software. This
kind of programs are chains of processes that transform raw data into valuable
information. In this work a Python framework for astronomical pipeline
generation is presented. It features a design pattern (Model-View-Controller)
on top of a SQL Relational Database capable of handling custom data models,
processing stages, and result communication alerts, as well as producing
automatic quality and structural measurements. This pat- tern provides
separation of concerns between the user logic and data models and the
processing flow inside the pipeline, delivering for free multi processing and
distributed computing capabilities. For the astronomical community this means
an improvement on previous data processing pipelines, by avoiding the
programmer deal with the processing flow, and parallelization issues, and by
making him focusing just in the algorithms involved in the successive data
transformations. This software as well as working examples of pipelines are
available to the community at this https URL | | Source: | arXiv, 1701.5566 | | Services: | Forum | Review | PDF | Favorites | |
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