Schema Compliant Consistency Management via Triple Graph Grammars and Integer Linear Programming

Author(s):  
Nils Weidmann ◽  
Anthony Anjorin

AbstractIn the field of Model-Driven Engineering, Triple Graph Grammars (TGGs) play an important role as a rule-based means of implementing consistency management. From a declarative specification of a consistency relation, several operations including forward and backward transformations, (concurrent) synchronisation, and consistency checks can be automatically derived. For TGGs to be applicable in realistic application scenarios, expressiveness in terms of supported language features is very important. A TGG tool is schema compliant if it can take domain constraints, such as multiplicity constraints in a meta-model, into account when performing consistency management tasks. To guarantee schema compliance, most TGG tools allow application conditions to be attached as necessary to relevant rules. This strategy is problematic for at least two reasons: First, ensuring compliance to a sufficiently expressive schema for all previously mentioned derived operations is still an open challenge; to the best of our knowledge, all existing TGG tools only support a very restricted subset of application conditions. Second, it is conceptually demanding for the user to indirectly specify domain constraints as application conditions, especially because this has to be completely revisited every time the TGG or domain constraint is changed. While domain constraints can in theory be automatically transformed to obtain the required set of application conditions, this has only been successfully transferred to TGGs for a very limited subset of domain constraints. To address these limitations, this paper proposes a search-based strategy for achieving schema compliance. We show that all correctness and completeness properties, previously proven in a setting without domain constraints, still hold when schema compliance is to be additionally guaranteed. An implementation and experimental evaluation are provided to support our claim of practical applicability.

Author(s):  
Daniel Fišer ◽  
Antonín Komenda

Mutex groups are defined in the context of STRIPS planning as sets of facts out of which, maximally, one can be true in any state reachable from the initial state. This work provides a complexity analysis showing that inference of mutex groups is as hard as planning itself (PSPACE-Complete) and it also shows a tight relationship between mutex groups and graph cliques. Furthermore, we propose a new type of mutex group called a fact-alternating mutex group (fam-group) of which inference is NP-Complete. We introduce an algorithm for the inference of fam-groups based on integer linear programming that is complete with respect to the maximal fam-groups and we demonstrate that fam-groups can be beneficial in the translation of planning tasks into finite domain representation, for the detection of dead-end state and for the pruning of spurious operators. The experimental evaluation of the pruning algorithm shows a substantial increase in a number of solved tasks in domains from the optimal deterministic track of the last two planning competitions (IPC 2011 and 2014).


1992 ◽  
Vol 7 (2) ◽  
pp. 744-752 ◽  
Author(s):  
Yuan-Yih Hsu ◽  
Kun-Long Ho ◽  
Chih-Chien Liang ◽  
Tsau-Shin Lai ◽  
Kung-Keng Chen ◽  
...  

Author(s):  
Duc-Hanh Dang ◽  
Martin Gogolla

Model transformation is an important building block for model-driven approaches. It puts forward a necessity and a challenge to specify and realize model transformation as well as to ensure the correctness of transformations. This paper proposes an OCL-based framework for model transformations. The formal foundation of the framework is the integration of Triple Graph Grammars and the Object Constraint Language (OCL). The OCL-based transformation framework offers an on-the-fly verification of model transformations and means for transformation quality assurance.


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