Interactive exploration and visual analytics for large spatiotemporal data using approximate query processing

Approximate query processing (AQP) provides fewer representative samples to approximate large amounts of data. Processing these smaller data subsets enables visualization 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 technique for enabling interactive visual analytics for large amounts of spatiotemporal data under the aforementioned challenges. First, we design, implement and evaluate a client-based visual analytics framework that progressively acquires spatiotemporal data from an AQP-absence server-side to client-based visualization systems so that interactive data exploration can be maintained on a client machine with modest computational power. Second, we design, implement, and evaluate an online sampling approach that selects samples of large spatiotemporal data in an unbiased manner and accordingly improves the accuracy of the associated estimates. Last, we design, implement and evaluate a difference assessment approach that compares approximate and exact spatial heatmap visualizations in terms of human perception. As such, information changes perceptible by users are well represented, and users can evaluate the reliability of approximate answers more easily. Our results show the superior performance 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.