Constructive visualization uses physical data units - tokens - to enable non-experts to create personalized visualizations engagingly. However, its physical nature limits efficiency and scalability. One potential solution to address this issue is autocomplete. By providing automated suggestions while still allowing for manual intervention, autocomplete can expedite visualization construction while maintaining expressivity. We conduct a speculative design study to examine how people would like to interact with a visualization authoring system that supports autocomplete. Our study identifies three types of autocomplete strategies and gains insights for designing future visualization authoring tools with autocomplete functionality. A free copy of this paper and all supplemental materials are available on our online repository: \url{https://osf.io/nu4z3/view_only=b69ba18933ca42cc9b7630e789a3f68c}.

翻译：构建式可视化通过物理数据单元（标记）使非专家用户能够以互动方式创建个性化可视化，但其物理特性限制了效率与可扩展性。自动补全技术是解决该问题的潜在方案——通过提供自动化建议同时允许人工干预，自动补全可在保持表现力的同时加速可视化构建。我们开展了一项推测性设计研究，探讨用户期望如何与支持自动补全的可视化创作系统进行交互。该研究识别出三种自动补全策略类型，并为设计未来具备自动补全功能的可视化创作工具提供了洞见。本文及所有补充材料的免费副本可在我们的在线存储库中获取：\url{https://osf.io/nu4z3/view_only=b69ba18933ca42cc9b7630e789a3f68c}