Paper Info
Title
Corral Framework: Trustworthy and Fully Functional Data Intensive Parallel Astronomical Pipelines.
Abstract
Data processing pipelines represent an important slice of the astronomical software library that include chains of processes that transform raw data into valuable information via data reduction and analysis. In this work we present Corral, a Python framework for astronomical pipeline generation. Corral features a Model-View-Controller design pattern on top of an SQL Relational Database capable of handling: custom data models; processing stages; and communication alerts, and also provides automatic quality and structural metrics based on unit testing. The Model-View-Controller provides concept separation between the user logic and the data models, delivering at the same time multi-processing and distributed computing capabilities. Corral represents an improvement over commonly found data processing pipelines in astronomysince the design pattern eases the programmer from dealing with processing flow and parallelization issues, allowing them to focus on the specific algorithms needed for the successive data transformations and at the same time provides a broad measure of quality over the created pipeline. Corral and working examples of pipelines that use it are available to the community at https://github.com/toros-astro.
Year
DOI
Venue
2017
10.1016/j.ascom.2017.07.003
Astronomy and Computing
Keywords
Field
DocType
Astroinformatics,Astronomical pipeline,Software and its engineering,Multiprocessing,Design patterns
SQL,Data mining,Data modeling,Data processing,Relational database,Computer science,Software design pattern,Software,Astroinformatics,Python (programming language),Database
Journal
Volume
ISSN
Citations 
20
2213-1337
1
PageRank 
References 
Authors
0.43
7
7
Name
Order
Citations
PageRank
Juan B. Cabral120.92
Bruno Sánchez220.92
Martin Beroiz310.43
Mariano Domínguez410.43
Marcelo Lares510.43
Sebastián Gurovich620.92
Pablo M. Granitto717617.75