Queries are the foundations of data intensive applications. In model-driven software engineering (MDE), model queries are core technologies of tools and transformations. As software models are rapidly increasing in size and complexity, traditional MDE tools frequently exhibit scalability issues that decrease productivity and increase costs.
While such scalability challenges are a constantly hot topic in the database community and recent efforts of the NoSQL movement have partially addressed many shortcomings, this happened at the cost of sacrificing the powerful declarative ad-hoc query capabilities of SQL. Unfortunately, this is a critical problem for MDE applications, as their queries can be significantly more complex than in general database applications. The applicability of NoSQL databases in MDE applications is subject for future research.
In my thesis, I aim to address this challenge by adapting incremental graph search techniques, known from the EMF-IncQuery framework, to a distributed cloud infrastructure. I present a novel architecture for distributed, scalable incremental query evaluation. IncQuery-D, the prototype system can scale up from a single node to a cluster of nodes that can handle very large models and complex queries efficiently. IncQuery-D is a backend-agnostic system, meaning that its query engine is independent from the data model of the underlying database.
The feasibility of the approach is supported by early experimental results with both an RDF and a graph database backend. The results prove that incremental query evaluation techniques can work efficiently in a distributed environment as well.