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Foundations for Streaming Model Transformations by Complex Event Processing.

István Dávid1, István Ráth2,3, Dániel Varró2,4

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Summary
This summary is machine-generated.

This study introduces streaming model transformations for high-volume, rapidly changing models. It integrates complex event processing (CEP) and reactive techniques for efficient event-driven model manipulation.

Keywords:
Change-driven transformationsComplex event processingLive modelsReactive transformationsStreaming model transformations

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Area of Science:

  • Computer Science
  • Software Engineering
  • Data Engineering

Background:

  • Traditional model transformations struggle with high-volume, rapidly changing data streams.
  • Efficiently recognizing transformation rules in live, infinite event streams is challenging.

Purpose of the Study:

  • To establish foundations for streaming model transformations.
  • To integrate incremental model querying, complex event processing (CEP), and reactive techniques for event-driven model manipulation.

Main Methods:

  • Developed an approach combining incremental model queries with CEP to detect patterns in model change events.
  • Utilized a reactive rule engine to execute transformations on identified complex event patterns.
  • Integrated these capabilities into a domain-specific language and execution engine within the Viatra framework.

Main Results:

  • Demonstrated a novel framework for processing streaming model transformations.
  • Successfully applied the approach to model engineering workflows and on-the-fly gesture recognition.

Conclusions:

  • The proposed foundations enable efficient, event-driven manipulation of streaming models.
  • The integrated Viatra framework provides a feasible solution for real-time model transformation challenges.