一种可扩展的带历史的内存有效查询模型
A Scalable Querying Scheme for Memory-efficient Runtime Models with History
摘要：
UML模型在运行时以所需的抽象级别提供系统的快照。通过与建模系统的因果关系，并采用模型驱动的工程技术，运行时模型支持（运行时）适应方案，其中来自先前快照的数据有助于做出更明智的决策。尽管如此，虽然运行时模型和基于模型的自适应技术一直是广泛研究的焦点，但将模型随时间的演变视为一等公民的方案最近才受到关注。因此，缺乏用于具有历史的这种运行时模型的复杂技术。我们提出了一个查询方案，集成的时间需求与增量模型查询，使可扩展的查询与历史的运行时模型。此外，我们的计划提供了这样的模型的内存效率的存储。通过将这两个功能集成到一个自适应循环中，我们可以通过运行时模型实现高效的历史感知自适应，我们提出了一个实现。
英文摘要：
Runtime models provide a snapshot of a system at runtime at a desired level of abstraction. Via a causal connection to the modeled system and by employing model-driven engineering techniques, runtime models support schemes for (runtime) adaptation where data from previous snapshots facilitates more informed decisions. Nevertheless, although runtime models and model-based adaptation techniques have been the focus of extensive research, schemes that treat the evolution of the model over time as a first-class citizen have only lately received attention. Consequently, there is a lack of sophisticated technology for such runtime models with history. We present a querying scheme where the integration of temporal requirements with incremental model queries enables scalable querying for runtime models with history. Moreover, our scheme provides for a memory-efficient storage of such models. By integrating these two features into an adaptation loop, we enable efficient history-aware self-adaptation via runtime models, of which we present an implementation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
