Pangloss implements \u201cOptimistic Visualization\u201d, a method that gives analysts confidence to use approximate results for exploratory data analysis. In this position paper, we outline some ways in which user experiences with an approximate visualization system did not match analysts\u2019 intuitions. These observations have implications for the design of future systems that expose uncertainty to users.<\/p>\n","protected":false},"excerpt":{"rendered":"
Pangloss implements \u201cOptimistic Visualization\u201d, a method that gives analysts confidence to use approximate results for exploratory data analysis. In this position paper, we outline some ways in which user experiences with an approximate visualization system did not match analysts\u2019 intuitions. These observations have implications for the design of future systems that expose uncertainty to users.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"msr_publishername":"","msr_edition":"CHI 2017 Workshop: \"Designing for Uncertainty in HCI: When Does Uncertainty 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