Leen Lambers's Books

On the operationalization of graph queries with generalized discrimination networks

By: Beyhl, Thomas,Blouin, Dominique,Giese, Holger,Lambers, Leen

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On the operationalization of graph queries with generalized discrimination networks

By: Beyhl, Thomas,Blouin, Dominique,Giese, Holger,Lambers, Leen

Graph queries have lately gained increased interest due to application areas such as social networks, biological networks, or model queries. For the relational database case the relational algebra and generalized discrimination networks have been studied to find appropriate decompositions into subqueries and ordering of these subqueries for query evaluation or incremental updates of query results. For graph database queries however there is no formal underpinning yet that allows us to find such suitable operationalizations. Consequently, we suggest a simple operational concept for the decomposition of arbitrary complex queries into simpler subqueries and the ordering of these subqueries in form of generalized discrimination networks for graph queries inspired by the relational case. The approach employs graph transformation rules for the nodes of the network and thus we can employ the underlying theory. We further show that the proposed generalized discrimination networks have the same expressive power as nested graph conditions.

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Universitätsverlag Potsdam

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ISBN 10

3869563729

ISBN 13

9783869563725

Symbolic model generation for graph properties

By: Schneider, Sven,Lambers, Leen,Orejas, Fernando

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Symbolic model generation for graph properties

By: Schneider, Sven,Lambers, Leen,Orejas, Fernando

Graphs are ubiquitous in Computer Science. For this reason, in many areas, it is very important to have the means to express and reason about graph properties. In particular, we want to be able to check automatically if a given graph property is satisfiable. Actually, in most application scenarios it is desirable to be able to explore graphs satisfying the graph property if they exist or even to get a complete and compact overview of the graphs satisfying the graph property. We show that the tableau-based reasoning method for graph properties as introduced by Lambers and Orejas paves the way for a symbolic model generation algorithm for graph properties. Graph properties are formulated in a dedicated logic making use of graphs and graph morphisms, which is equivalent to firstorder logic on graphs as introduced by Courcelle. Our parallelizable algorithm gradually generates a finite set of so-called symbolic models, where each symbolic model describes a set of finite graphs (i.e., finite models) satisfying the graph property. The set of symbolic models jointly describes all finite models for the graph property (complete) and does not describe any finite graph violating the graph property (sound). Moreover, no symbolic model is already covered by another one (compact). Finally, the algorithm is able to generate from each symbolic model a minimal finite model immediately and allows for an exploration of further finite models. The algorithm is implemented in the new tool AutoGraph.

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Universitätsverlag Potsdam

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3869563966

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9783869563961

Automatic verification of behavior preservation at the transformation level for relational model transformation

By: Dyck, Johannes,Giese, Holger,Lambers, Leen

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Automatic verification of behavior preservation at the transformation level for relational model transformation

By: Dyck, Johannes,Giese, Holger,Lambers, Leen

The correctness of model transformations is a crucial element for model-driven engineering of high quality software. In particular, behavior preservation is the most important correctness property avoiding the introduction of semantic errors during the model-driven engineering process. Behavior preservation verification techniques either show that specific properties are preserved, or more generally and complex, they show some kind of behavioral equivalence or refinement between source and target model of the transformation. Both kinds of behavior preservation verification goals have been presented with automatic tool support for the instance level, i.e. for a given source and target model specified by the model transformation. However, up until now there is no automatic verification approach available at the transformation level, i.e. for all source and target models specified by the model transformation. In this report, we extend our results presented in [27] and outline a new sophisticated approach for the automatic verification of behavior preservation captured by bisimulation resp. simulation for model transformations specified by triple graph grammars and semantic definitions given by graph transformation rules. In particular, we show that the behavior preservation problem can be reduced to invariant checking for graph transformation and that the resulting checking problem can be addressed by our own invariant checker even for a complex example where a sequence chart is transformed into communicating automata. We further discuss today's limitations of invariant checking for graph transformation and motivate further lines of future work in this direction.

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126

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Universitätsverlag Potsdam

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ISBN 10

3869563915

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9783869563916

Toward Bridging the Gap Between Formal Semantics and Implementation of Triple Graph Grammars

By: Holger Giese,Stefan Hildebrandt,Leen Lambers

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Toward Bridging the Gap Between Formal Semantics and Implementation of Triple Graph Grammars

By: Holger Giese,Stefan Hildebrandt,Leen Lambers

The correctness of model transformations is a crucial element for the model-driven engineering of high quality software. A prerequisite to verify model transformations at the level of the model transformation specification is that an unambiguous formal semantics exists and that the employed implementation of the model transformation language adheres to this semantics. However, for existing relational model transformation approaches it is usually not really clear under which constraints particular implementations are really conform to the formal semantics. In this paper, we will bridge this gap for the formal semantics of triple graph grammars (TGG) and an existing efficient implementation. Whereas the formal semantics assumes backtracking and ignores non-determinism, practical implementations do not support backtracking, require rule sets that ensure determinism, and include further optimizations. Therefore, we capture how the considered TGG implementation realizes the transformation by means of operational rules, define required criteria and show conformance to the formal semantics if these criteria are fulfilled. We further outline how static analysis can be employed to guarantee these criteria.

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