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Streaming Model Transformations By Complex Event Processing

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8767)

Abstract

Streaming model transformations represent a novel class of transformations dealing with models whose elements are continuously produced or modified by a background process [1]. 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.

Keywords

Streaming model transformations complex event processing live models change-driven transformations 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Measurement and Information SystemsBudapest University of Technology and EconomicsBudapestHungary
  2. 2.DIROUniversité de MontréalCanada
  3. 3.MSDL, Dept. of Computer ScienceMcGill UniversityMontréalCanada

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