The examples below correspond to updated versions of the old examples.
Introductory example: school
The school example features a simple EMF model and some simple graph patterns to show the very basics of using EMF-IncQuery. Read this first before anything else!
Introductory example: BPMN validation
The BPMN validation example introduces the IncQuery Validation Engine, by which incrementally evaluated, on-the-fly well-formedness validation rules can be specified for any EMF model.
Metamodel pattern matching example: Ecore Queries
Have you ever tried to query a meta-model, i.e. a model consisting of EClasses, EAttributes, and EReferences, against a meta-model pattern? For an example, look here. In this case, we want to look for a certain sub-configuration in an ECore metamodel, where two EClasses (each having an EAttribute of type EString) are connected by a 0-* EReference.
Use-cases of this technique include meta-transformation, meta-model analysis, higher-order transformations where transformation rules are generated/parameterized by metamodel analysis etc.
Using queries for derived features
We demonstrate how our high performance queries can be easily integrated with other EMF tools using an entirely new case study in which EMF-IncQuery is deeply integrated into the EMF modeling infrastructure to facilitate the incremental evaluation of derived EAttributes and EReferences.
Running IncQuery as a headless RCP application
IncQuery can be called without any graphical user interface or even a complete Eclipse installation. In this example we take an existing IncQuery project and create an Eclipse Application that can be executed from a console (command prompt) to print the matches for an arbitrary input model file.
Query-driven soft interconnections of EMF models
We demonstrate how the incremental query evaluation of EMF-IncQuery can be used to maintain soft interconnections between EMF models stored even if they are stored separately, moved or modified without the all the corresponding models loaded at all times.
Real-time gesture recognition with Jnect and the Esper CEP engine
This demo was prepared for EclipseCon Europe 2012. Using live input from Jnect, the body model of a human user is processed by EMF-IncQuery in real time to recognize gestures. Additionally, the Esper Complex Event Processor is integrated into the system to allow the recognition of gesture sequences in the event stream generated by IncQuery.