scholarly journals Termination Criteria for Model Transformation

Author(s):  
Hartmut Ehrig ◽  
Karsten Ehrig ◽  
Juan de Lara ◽  
Gabriele Taentzer ◽  
Dániel Varró ◽  
...  
2014 ◽  
Vol 24 (4) ◽  
Author(s):  
DÉNES BISZTRAY ◽  
REIKO HECKEL

In model transformations, where source models are automatically translated into target models or code, termination is necessary for the transformation to be well defined. There are a number of specific termination criteria that can be used when specifying model transformations by graph transformation, though termination is undecidable in general. Unfortunately, and particularly for large and heterogeneous specifications, it is often not possible to use a single termination criterion. In this paper, we propose an approach that applies different criteria to suitable subsets of rules so that termination can be shown locally using the most suitable technique for each subset. Global termination then follows if certain causal dependencies between rules in different subsets are acyclic. The theory is developed at the level of typed attributed graphs, and is motivated and illustrated by a case study translating UML activity diagrams to CSP.


2015 ◽  
Vol 10 (12) ◽  
pp. 1186 ◽  
Author(s):  
Yassine Rhazali ◽  
Y. Hadi ◽  
A. Mouloudi
Keyword(s):  

2011 ◽  
Vol 22 (2) ◽  
pp. 195-210 ◽  
Author(s):  
Xiao HE ◽  
Zhi-Yi MA ◽  
Yan ZHANG ◽  
Wei-Zhong SHAO

2008 ◽  
Vol 19 (9) ◽  
pp. 2203-2217 ◽  
Author(s):  
Tian ZHANG ◽  
Yan ZHANG ◽  
Xiao-Feng YU ◽  
Lin-Zhang WANG ◽  
Xuan-Dong LI

2009 ◽  
Vol 20 (8) ◽  
pp. 2113-2123 ◽  
Author(s):  
Jin-Kui HOU ◽  
Hai-Yang WANG ◽  
Jun MA ◽  
Jian-Cheng WAN ◽  
Xiao YANG

2010 ◽  
Vol 27 (3) ◽  
pp. 207-216 ◽  
Author(s):  
Luis Iribarne ◽  
Nicolás Padilla ◽  
Javier Criado ◽  
José-Andrés Asensio ◽  
Rosa Ayala

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 159-173
Author(s):  
Simone Fontana ◽  
Domenico Giorgio Sorrenti

Probabilistic Point Clouds Registration (PPCR) is an algorithm that, in its multi-iteration version, outperformed state-of-the-art algorithms for local point clouds registration. However, its performances have been tested using a fixed high number of iterations. To be of practical usefulness, we think that the algorithm should decide by itself when to stop, on one hand to avoid an excessive number of iterations and waste computational time, on the other to avoid getting a sub-optimal registration. With this work, we compare different termination criteria on several datasets, and prove that the chosen one produces very good results that are comparable to those obtained using a very large number of iterations, while saving computational time.


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