Streaming model transformations represent a novel class of transformations to manipulate models whose elements are continuously produced or modified in high volume and with rapid rate of change. Executing streaming transformations requires efficient techniques to recognize activated transformation rules over a live model and a potentially infinite stream of events. In this paper, we propose foundations of streaming model transformations by innovatively integrating incremental model query, complex event processing (CEP) and reactive (event-driven) transformation techniques. Complex event processing allows to identify relevant patterns and sequences of events over an event stream. Our approach enables event streams to include model change events which are automatically and continuously populated by incremental model queries. Furthermore, a reactive rule engine carries out transformations on identified complex event patterns. We provide an integrated domain-specific language with precise semantics for capturing complex event patterns and streaming transformations together with an execution engine, all of which is now part of the Viatra reactive transformation framework. We demonstrate the feasibility of our approach with two case studies: one in an advanced model engineering workflow; and one in the context of on-the-fly gesture recognition.

中文翻译：

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通过复杂事件处理进行流式模型转换的基础。

流模型转换代表一类新颖的转换，用于处理模型，这些模型的元素不断大量生产或修改且变化迅速。执行流转换需要有效的技术来识别活动模型和事件的潜在无限流上的激活转换规则。在本文中，我们通过创新地集成增量模型查询，复杂事件处理（CEP）和反应式（事件驱动）转换技术，为流模型转换提供了基础。复杂的事件处理允许在事件流上标识事件的相关模式和序列。我们的方法使事件流能够包含由增量模型查询自动连续填充的模型更改事件。此外，反应式规则引擎对已识别的复杂事件模式进行转换。我们提供具有精确语义的集成领域特定语言，用于捕获复杂的事件模式和流转换以及执行引擎，所有这些现在都已成为Viatra反应转化框架。我们通过两个案例研究来证明我们的方法的可行性：一个在高级模型工程工作流程中；另一个在高级模型工程工作流程中。一种是即时手势识别。