Multiple-view visualizations (MVs) have been widely used for visual analysis. Each view shows some part of the data in a usable way, and together multiple views enable a holistic understanding of the data under investigation. For example, an analyst may check a social network graph, a map of sensitive locations, a table of transaction records, and a collection of reports to identify suspicious activities. While each view is designed to preserve its own visual context with visible borders or perceivable spatial distance from others, the key to solving real-world analysis problems often requires "breaking" such boundaries, and further integrating and synthesizing the data scattered across multiple views. This calls for blending the boundaries in MVs, instead of simply breaking them, which brings key questions: what are possible boundaries in MVs, and what are design options that can support the boundary blending in MVs? To answer these questions, we present three boundaries in MVs: 1) data boundary, 2) representation boundary, and 3) semantic boundary, corresponding to three major aspects regarding the usage of MVs: encoded information, visual representation, and interpretation. Then, we discuss four design strategies (highlighting, linking, embedding, and extending) and their pros and cons for supporting boundary blending in MVs. We conclude our discussion with future research opportunities.

翻译：多视图可视化（MVs）已被广泛应用于可视化分析中。每个视图以可用方式展示部分数据，多个视图共同实现对所研究数据的整体理解。例如，分析师可能需要检查社交网络图、敏感位置地图、交易记录表格以及报告集合，以识别可疑活动。尽管每个视图设计为通过可见边框或可感知的空间距离保持自身视觉上下文，但解决实际分析问题的关键往往需要"打破"此类边界，进一步整合与综合分散在多视图中的数据。这需要融合多视图中的边界，而非简单打破，由此引发关键问题：多视图中可能存在哪些边界？支持多视图边界融合的设计方案有哪些？为回答这些问题，我们提出多视图中的三种边界：1）数据边界，2）表征边界，3）语义边界，分别对应多视图使用的三个主要维度：编码信息、视觉表征与解释。随后，我们讨论四种设计策略（高亮、关联、嵌入与扩展）及其在支持多视图边界融合中的优劣。最后，我们展望未来研究方向。