AI 中文总结
本研究提出抽象符号工程方法，将其应用于DCR图形成DeCleaR，通过用高层级语义透明构造替代底层配置，提升了概念模型的可理解性及用户对DCR图的相关质量与偏好。
AI 中文摘要
概念建模支持复杂系统属性的设计、分析与交流，但当领域级抽象必须通过主要为语义一致性所需的底层构造编码时，概念模型可能难以理解。现有工作主要改进现有单个构造的视觉呈现，本研究将焦点从单个构造转向构造的重复配置，提出抽象符号工程作为与语言无关的方法，用更高层级、语义透明的构造替代此类配置，该方法包含模式识别、模式形式化、视觉符号设计与实证验证四个步骤。本研究将其应用于动态条件响应（Dynamic Condition Response, DCR）图，该图中常见工作流模式需要复杂的底层配置，由此产生的扩展版本DeCleaR用紧凑的基于模式的抽象替代此类配置。实证验证结果显示，与标准DCR图相比，DeCleaR提升了感知实证质量、实用质量及用户偏好。
英文摘要
Conceptual modeling supports the design, analysis, and communication of the properties of complex systems, yet conceptual models can be difficult to understand when domain-level abstractions must be encoded through low-level constructs required mainly for semantic conformity. Prior work has mainly improved how existing individual constructs are visually represented. We shift the focus from individual constructs to recurring configurations of constructs, and propose abstract notation engineering as a language-agnostic method for replacing such configurations with higher-level, semantically transparent constructs. The method comprises pattern identification, pattern formalization, visual notation design, and empirical validation. We instantiate it for Dynamic Condition Response (DCR) graphs, where common workflow patterns require elaborate low-level configurations. The resulting extension, DeCleaR, replaces such configurations with compact pattern-based abstractions. The results of our empirical validation show that DeCleaR improves perceived empirical quality, pragmatic quality, and user preference over standard DCR graphs.
