Incremental Backward Change Propagation of View Models by Logic Solvers

View models are key concepts of domain-specific modeling to provide task-specific focus (e.g., power or communication architecture of a system) to the designers by highlighting only the relevant aspects of the system. View models can be specified by unidirectional forward transformations (frequently captured by graph queries), and automatically maintained upon changes of the underlying source model using incremental transformation techniques. However, tracing back complex changes from one or more abstract view to the underlying source model is a challenging task, which, in general, requires the simultaneous analysis of transformation specifications and well-formedness constraints to create valid changes in the source model. In this paper we introduce a novel delta-based backward transformation technique using SAT solvers to synthetize valid and consistent change candidates in the source model, where only forward transformation rules are specified for the view models

Spatio-Temporal Model-Checking of Cyber-Physical Systems Using Graph Queries

We explore the application of graph database technology to spatio-temporal model checking of cooperating cyber-physical systems-of-systems such as vehicle platoons. We present a translation of spatio-temporal automata (STA) and the spatio-temporal logic STAL to semantically equivalent property graphs and graph queries respectively. We prove a sound reduction of the spatio-temporal verification problem to graph database query solving. The practicability and efficiency of this approach is evaluated by introducing NeoMC, a prototype implementation of our explicit model checking approach based on Neo4j. To evaluate NeoMC we consider case studies of verifying vehicle platooning models. Our evaluation demonstrates the effectiveness of our approach in terms of execution time and counterexample detection

Model-driven engineering of an openCypher engine: using graph queries to compile graph queries

Graph database systems are increasingly adapted for storing and processing heterogeneous network-like datasets. Many challenging applications with near real-time requirements - such as financial fraud detection, on-the-fly model validation and root cause analysis - can be formalised as graph problems and tackled with graph databases efficiently. However, as no standard graph query language has yet emerged, users are subjected to the possibility of vendor lock-in. The openCypher group aims to define an open specification for a declarative graph query language. However, creating an openCypher-compatible query engine requires significant research and engineering efforts. Meanwhile, model-driven language workbenches support the creation of domain-specific languages by providing high-level tools to create parsers, editors and compilers. In this paper, we present an approach to build a compiler and optimizer for openCypher using model-driven technologies, which allows developers to define declarative optimization rules