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- W3048742720 abstract "Approximate query processing (AQP) provides fewer representative samples toapproximate large amounts of data. Processing these smaller data subsets enablesvisualization systems to provide end-users with real-time responses. However, challenges arise for real-world users in adopting AQP-based visualization systems, e.g.,the absence of AQP modules in mainstream commercial databases, erroneous estimations caused by sampling bias, and end-user uncertainty when interpreting approximate query results. In this dissertation, we present an AQP-centered techniquefor enabling interactive visual analytics for large amounts of spatiotemporal dataunder the aforementioned challenges. First, we design, implement and evaluate aclient-based visual analytics framework that progressively acquires spatiotemporaldata from an AQP-absence server-side to client-based visualization systems so thatinteractive data exploration can be maintained on a client machine with modest computational power. Second, we design, implement, and evaluate an online samplingapproach that selects samples of large spatiotemporal data in an unbiased mannerand accordingly improves the accuracy of the associated estimates. Last, we design,implement and evaluate a difference assessment approach that compares approximateand exact spatial heatmap visualizations in terms of human perception. As such, information changes perceptible by users are well represented, and users can evaluatethe reliability of approximate answers more easily. Our results show the superiorperformance of our proposed AQP-centered technique in terms of speed, accuracy,and user trust, as compared to a baseline of state-of-the-art solutions." @default.
- W3048742720 created "2020-08-18" @default.
- W3048742720 creator A5035099483 @default.
- W3048742720 date "2020-08-13" @default.
- W3048742720 modified "2023-09-28" @default.
- W3048742720 title "Interactive exploration and visual analytics for large spatiotemporal data using approximate query processing" @default.
- W3048742720 doi "https://doi.org/10.25394/pgs.12783458.v1" @default.
- W3048742720 hasPublicationYear "2020" @default.
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