  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 https://osf.io/nu4z3/?view_only=594baee54d114a99ab381886fb32a126
