Most engineers and designers prefer to use large drawing boards such as whiteboards or flip charts for the initial collaborative sketching of a system’s models. Large interactive displays have recently begun to replace these physical drawing boards, blurring the line between freehand sketching and toolkit-aided modeling. While digital boards offer more flexibility in drawing and navigating models, they must also provide appropriate cognitive support for frequent shifts of focus and navigation between related artifacts. Furthermore, automated assistance in uncovering potential inconsistencies and contradictions between model sketches would be beneficial so that users do not get lost amid their sketches. In this paper, we discuss an approach to create relationships between the elements of informal hand-drawn sketches on large interactive displays by combining fuzzy search with classic information retrieval techniques. The identification and maintenance of relationships is particularly challenging because we are working with hand-drawn and hand-lettered model sketches rather than the syntactically clean models created with digital modeling toolkits. We evaluated our approach by analyzing 89 model sketches from 16 industry projects and found that it identifies relations between sketched model elements with high precision and recall.

中文翻译：

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评估非正式手绘草图的可追溯性方法

大多数工程师和设计师更喜欢使用大型绘图板，如白板或活动挂图，用于系统模型的初始协作草图。大型交互式显示器最近开始取代这些物理绘图板，模糊了手绘草图和工具包辅助建模之间的界限。虽然数字板在绘制和导航模型方面提供了更大的灵活性，但它们还必须为相关工件之间的频繁转移和导航提供适当的认知支持。此外，自动协助发现模型草图之间的潜在不一致和矛盾将是有益的，这样用户就不会在草图中迷失方向。在本文中，我们讨论了一种通过将模糊搜索与经典信息检索技术相结合，在大型交互式显示器上创建非正式手绘草图元素之间关系的方法。关系的识别和维护特别具有挑战性，因为我们正在使用手绘和手写的模型草图，而不是使用数字建模工具包创建的句法干净的模型。我们通过分析来自 16 个行业项目的 89 个模型草图来评估我们的方法，发现它以高精度和召回率识别草图模型元素之间的关系。关系的识别和维护特别具有挑战性，因为我们正在使用手绘和手写的模型草图，而不是使用数字建模工具包创建的句法干净的模型。我们通过分析来自 16 个行业项目的 89 个模型草图来评估我们的方法，发现它以高精度和召回率识别草图模型元素之间的关系。关系的识别和维护特别具有挑战性，因为我们正在使用手绘和手写的模型草图，而不是使用数字建模工具包创建的句法干净的模型。我们通过分析来自 16 个行业项目的 89 个模型草图来评估我们的方法，发现它以高精度和召回率识别草图模型元素之间的关系。