Version management in Gypsy

Author(s):  
Ellis S. Cohen ◽  
Dilip A. Soni ◽  
Raimund Gluecker ◽  
William M. Hasling ◽  
Robert W. Schwanke ◽  
...  
Keyword(s):  
2012 ◽  
Vol 14 (4) ◽  
pp. 442-447
Author(s):  
Peizhong WANG ◽  
Weidong YAN ◽  
Hui BIAN ◽  
Bin SUN ◽  
Xinlu MA

2020 ◽  
Vol 53 (2) ◽  
pp. 7827-7832
Author(s):  
K. Land ◽  
B. Vogel-Heuser ◽  
A. Gallasch ◽  
M. Sagerer ◽  
D. Förster ◽  
...  

Author(s):  
Vijay R Sonawane ◽  
D.R. Rao

<span>The efficient management of the dynamic XML documents is a complex area of research. The changes and size of the XML documents throughout its lifetime are limitless. Change detection is an important part of version management to identify difference between successive versions of a document. Document content is continuously evolving. Users wanted to be able to query previous versions, query changes in documents, as well as to retrieve a particular document version efficiently. In this paper we provide comprehensive comparative analysis of various control schemes for change detection and querying dynamic XML documents.</span>


Author(s):  
Aaron Peikert ◽  
Andreas M. Brandmaier

In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible. While the research community increasingly recognizes dynamic document generation and version management as tools to ensure reproducibility, we demonstrate with practical examples that these alone are not sufficient to ensure long-term computational reproducibility. Combining containerization, dependence management, version management, and dynamic document generation, the proposed workflow increases scientific productivity by facilitating later reproducibility and reuse of code and data.


Author(s):  
I-Min A. Chen ◽  
Victor M. Markowitz ◽  
Stanley Letovsky ◽  
Peter Li ◽  
Kenneth H. Fasman

Author(s):  
Chien Shu-Yao ◽  
Vassilis J. Tsotras ◽  
Carlo Zaniolo

OOIS 2000 ◽  
2001 ◽  
pp. 238-252
Author(s):  
D. Janaki Ram ◽  
M. Sreekanth ◽  
A. Ananda Rao

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