The model driven software development can be found in many aspect of the industry due to its advantages. When you design safety-critical embedded systems, you need safe, generated code. You can reach this with complex and detailed big models. This intensive growth often leads to serious scalability problems; they increase the cost and time of the development. We use complex queries to verify the structure of the models; an important factor is to run these queries effectively. When models are changing frequently, and you want to get immediate results for the predefined queries, you should use incremental query engines. Thus, the designer of the model gets an instantaneous feedback, and can adjust the model, according to the query results.
There are already available repositories and incremental query algorithms, but there is no way to handle those together, there is no middle layer. I created this layer in this thesis.
The created Query-Data Middleware can serve multiple query engine, and repository, so it can be used to run multiple queries on multiple repositories. I have implemented an incremental query engine, which can be used to build up a Rete net, so the result of the predefined queries will be updated (when the model changes) and available at any time. The person, who uses this middle layer, is able to add new queries, and repositories to the system. He can also add new data to the system, extending the contents of the repositories. I proved the functional operability of the system through a performance measurement, which used different sizes of RDF/OWL models. I measured the service efficiency (needed by the queries) of the repositories.