Streaming model transformations represent a novel class of transformations dealing with models whose elements are continuously produced or modified by a background process . Executing streaming transformations requires efficient techniques to recognize the activated transformation rules on a potentially infinite input stream. Detecting a series of events triggered by compound structural changes is especially challenging for a high volume of rapid modifications, a characteristic of an emerging class of applications built on runtime models. In this paper, we propose a novel approach for streaming model transformations by combining incremental model query techniques with complex event processing (CEP) and reactive (event-driven) transformations. The event stream is automatically populated from elementary model changes by the incremental query engine, and the CEP engine is used to identify complex event combinations, which are used to trigger the execution of transformation rules. We demonstrate our approach in the context of automated gesture recognition over live models populated by Kinect sensor data.
|Number of pages||16|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2014|
- Change-driven transformations
- Complex event processing
- Live models
- Streaming model transformations