This work introduces a concept for rule based model verification using a graph database on the example of Neo4j and its query language Cypher. An approach is provided that allows to define verification rules using a graph query language to detect transformation errors within a given domain model. The approach is presented based on a running example, showing its capability of detecting randomly generated errors in a transformation process. Additionally, the method’s performance is evaluated using multiple subsets of the IMDb movie data with a maximum of 17,000,000 nodes and 41,000,000 relationships. This performance evaluation is carried out in comparison to the Object Constraint Language, showing advantages in the context of highly connected datasets with a high number of nodes. Another benefit is the utilization of a well established graph database as verification tool without any need for re-implementing graph and pattern matching logic.

Original languageEnglish
Publication statusPublished - Sept 2022
EventEuropean Modeling & Simulation Symposium 2022 - Rom, Italy
Duration: 19 Sept 202221 Sept 2022
Conference number: 34


ConferenceEuropean Modeling & Simulation Symposium 2022
Abbreviated titleEMSS
Internet address


  • Cypher
  • Graph-based Modeling
  • Model Verification
  • Neo4j


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