TemporalEMF adds native temporal support for models. Models are automatically treated as temporal models and can be subjected to temporal queries to retrieve the model contents at different points in time. Behind the scenes, the history of a model is transparently stored in a NoSQL database.
Most NoSQL database systems do not require the definition of schemas but this does not mean such schema does not (implicitly) exist. We have implemented a model-driven reverse engineering approach to infer such NoSQL implicit schemas
Summary of our contributions towards a scalable query and transformation modeling framework able to handle very large models
Gremlin-ATL is a scalable and efficient model-to-model transformation framework that translates ATL transformations into Gremlin, a query language supported by several NoSQL databases
NeoEMF is a multi-database model persistence solution, that is able to store models in several kind of NoSQL datastores, including graph, map and column databases
Ten thngs to keep in mind when mixing modeling and big data. Modeling is as important as ever when dealing with big data but it must be adapted
Few solutions target UML to NoSQL code-generation and even less consider OCL constraints. We present a UML/OCL transformation to Blueprints, an abstraction layer on top of a variety of graph databases
How to speed up the access and queries on large models thanks to our language (and execution environment) to define prefetching/caching plans for specific modeling scenarios
New version of our tool able to infer the shared schema among a set of schemaless JSON Documents
Mogwaï is framework to store large models in a GraphDB NoSQL backend (thanks to NeoEMF) and efficiently query those models by means of a OCL to Gremlin (a query language for some NoSQL databases) transformation.