Pattern-based model-to-model transformation: Handling attribute conditions

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02408-5_7Proceedings of Second International Conference, ICMT 2009, Zurich, Switzerland, June 29-30, 2009Pattern-based model-to-model transformation is a new approach for specifying transformations in a declarative, relational and formal style. The language relies on patterns describing allowed or forbidden relations between two models, which are compiled into operational mechanisms to perform forward and backward transformations. In this paper, we extend the approach for handling attribute conditions expressed in some suitable logic, adapt the operational mechanisms based on graph transformation to relax attribute handling by constraint solving, and discuss heuristics for the compilation of patterns into rules.Work supported by the Spanish Ministry of Science and Innovation, projects METEORIC (TIN2008-02081),MODUWEB (TIN2006-09678) and FORMALISM (TIN2007-66523).Moreover, part of this work was done during a sabbatical leave of the third author at TU Berlin, with financial support from the Ministerio de Ciencia e Innovaci´on (grant ref. PR2008-0185). We thank the referees for their useful comment

Pattern-based model transformation: a metamodel-based approach to model evolution

Software systems continue to grow in complexity at a rapid pace, creating systems that are complex to build and evolve. The problems that accompany changes in requirements, system upgrades, and error correction produce a desire for software evolution methods that increase the efficiency and effectiveness of adapting complex software to changes. As software systems evolve, design models must be modified to accommodate the required changes. Techniques that control the changes to models in a systematic manner are a key to model evolution. A process that improves the ability to effectively modify a design, thereby enhancing design qualities, supports the need for improved model evolution techniques. Design patterns are common forms of reusable design experiences. They offer solutions to common design problems, reduce complexity by naming and defining abstractions, and provide a foundation for building reusable software. Well-known pattern solutions are expressed in a natural language as fragments of code which are sometimes difficult to understand and implement by software modelers. With increased focus on development of model-driven approaches, rigorous descriptions of design patterns that capture solutions during design instead of implementation are needed. This research defines an approach for the transformation of models that supports controlled model evolution. More precisely, a process for capturing design patterns in UML class diagrams is defined. This process involves defining a metamodel-level representation which specifies how a software developer can introduce design patterns into existing design models. We defined transformation patterns as an extension of the UML metamodel to characterize source and target model elements. The transformation pattern consists of specialized metamodel elements that specify the structure of source and target metamodels. Transformation patterns were specified for the Abstract Factory, Bridge and Visitor design patterns to show how the model-level transformations can be perform on patterns that represent different functionalities. We developed an action language to specify constructs which add, delete, retrieve and connect model elements. We used the constructs of the action language to define transformation specifications that implement model-level transformations on class diagrams. To determine the potential of this approach we manually implemented the transformation specification on a UML design

Correctness, completeness and termination of pattern-based model-to-model transformation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-03741-2_26Proceedings of Third International Conference, CALCO 2009, Udine, Italy, September 7-10, 2009.Model-to-model (M2M) transformation consists in trans- forming models from a source to a target language. Many transformation languages exist, but few of them combine a declarative and relational style with a formal underpinning able to show properties of the transformation. Pattern-based transformation is an algebraic, bidirectional, and relational approach to M2M transformation. Specifications are made of patterns stating the allowed or forbidden relations between source and target models, and then compiled into low level operational mechanisms to perform source-to-target or target-to-source transformations. In this paper, we study the compilation into operational triple graph grammar rules and show: (i) correctness of the compilation of a specification without negative patterns; (ii) termination of the rules, and (iii) completeness, in the sense that every model considered relevant can be built by the rules.Work supported by the Spanish Ministry of Science and Innovation, projects METEORIC (TIN2008-02081), MODUWEB (TIN2006-09678) and FORMALISM (TIN2007-66523). Moreover, part of this work was done during a sabbatical leave of the first author at TU Berlin, with financial support from the Spanish Ministry of Science and Innovation (grant ref. PR2008-0185). We thank the referees for their useful comment

Parallelization of Graph Transformation Based on Incremental Pattern Matching

oai:journal.ub.tu-berlin.de:article/265Graph transformation based on incremental pattern matching explicitly stores all occurrences of patterns (left-hand side of rules) and updates this result cache upon model changes. This allows instantaneous pattern queries at the expense of costlier model manipulation and higher memory consumption. Up to now, this incremental approach has considered only sequential execution despite the inherently distributed structure of the underlying match caching mechanism. The paper explores various possibilities of parallelizing graph transformation to harness the power of modern multi-core, multi-processor computing environments: (i) incremental pattern matching enables the concurrent execution of model manipulation and pattern matching; moreover, (ii) pattern matching itself can be parallelized along caches

Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks

Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks (RNN) to handle raw skeletons only focus on the contextual dependency in the temporal domain and neglect the spatial configurations of articulated skeletons. In this paper, we propose a novel two-stream RNN architecture to model both temporal dynamics and spatial configurations for skeleton based action recognition. We explore two different structures for the temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed according to human body kinematics. We also propose two effective methods to model the spatial structure by converting the spatial graph into a sequence of joints. To improve generalization of our model, we further exploit 3D transformation based data augmentation techniques including rotation and scaling transformation to transform the 3D coordinates of skeletons during training. Experiments on 3D action recognition benchmark datasets show that our method brings a considerable improvement for a variety of actions, i.e., generic actions, interaction activities and gestures.Comment: Accepted to IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 201

A Query Language With the Star Operator

Model pattern matching is an important operation in model transformation and therefore in model-driven development tools. In this paper we present a pattern based approach that includes a star operator that can be used to represent recursive or hierarchical structures in models. We also present a matching algorithm, motivating examples and we discuss its implementation in a modeling tool

PATTERN MATCHING IN METAMODEL-BASED MODEL TRANSFORMATION SYSTEMS

The vision of the OMG´s Model-Driven Architecture (MDA) has necessitated the extensive research of model compilers, which are able to process graph-based visual models specified mainly in the Unified Modeling Language (UML). A possible mechanism for the realization of MDA model compilers can be graph rewriting-based transformation approach. Previous work has introduced the tool Visual Modeling and Transformation System, which uses graph rewriting as transformation mechanism, but the pattern language of the rewriting rules consists of UML class diagram elements instead of object diagram level patterns. This paper provides the algorithmic background for the application of these rules specified by the class diagram elements. To achieve that, it examines the allowed instantiation configuration based on the UML standard, and supplies a constructive algorithm to compute the allowed number of the objects participating in a valid instantiation of a class model. Furthermore, starting from the VF2 algorithm, the pattern matching algorithm for the left hand side of the metamodel-based rewriting rule is provided via several optimization steps examined

A framework for efficient model transformations

The reported productivity gains while using models and model transformations to develop entire systems, after almost a decade of experience applying model-driven approaches for system development, are already undeniable benefits of this approach. However, the slowness of higher-level, rule based model transformation languages hinders the applicability of this approach to industrial scales. Lower-level, and efficient, languages can be used but productivity and easy maintenance seize to exist. The abstraction penalty problem is not new, it also exists for high-level, object oriented languages but everyone is using them now. Why is not everyone using rule based model transformation languages then? In this thesis, we propose a framework, comprised of a language and its respective environment, designed to tackle the most performance critical operation of high-level model transformation languages: the pattern matching. This framework shows that it is possible to mitigate the performance penalty while still using high-level model transformation languages