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Cited by 824

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Visual information seeking: tight coupling of dynamic query filters with starfield displays

Ahlberg, Christopher, Shneiderman, Ben·1994·Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Abstract

ABSTRACT This paper offers new principles for visual information seeking (VIS). A key concept is to support browsing, which is distinguished from familiar query composition and information retrieval because of its emphasis on rapid filtering to reduce result sets, progressive refinement of search parameters, continuous reformulation of goals, and visual scanning to identify results. VIS principles developed include: dynamic query filters (query parameters are rapidly adjusted with sliders, buttons, maps, etc.), starfield displays (two-dimensional scatterplots to structure result sets and zooming to reduce clutter), and tight coupling (interrelating query components to preserve display invariants and support progressive refinement combined with an emphasis on using search output to foster search input). A FilmFinder prototype using a movie database demonstrates these principles in a VIS environment.

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Metadata

Authors
Ahlberg, C., & Shneiderman, B.
Venue
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
1994-04-24
Pages
313-317
OpenAlex
W2102337314
Cited by
824
References
0
FWCI
50.29
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Visual information seeking: tight coupling of dynamic query filters with starfield displays. https://app.cochat.ai/papers/10.1145/191666.191775
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Ahlberg, C., & Shneiderman, B. (1994). Visual information seeking: tight coupling of dynamic query filters with starfield displays. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/191666.191775

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.