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• W4206781581 abstract "Partially due to technological advancements as well as the availability of affordable global positioning system (GPS) and cellular devices, more spatio-temporal data can be generated and collected. The presence of spatial and temporal dimensions uniquely differentiate spatio-temporal data from classical data as spatio-temporal data points are structurally related in the context of space and time. In this paper, we present a solution for privacy-preserving publishing and visualization of spatiotemporal big data information. Specifically, it consists of a spatiotemporal hierarchy model (STHM) for some common big data management tasks such as visualization. Our data visualizer provides actionable insight to enhance data-driven decision making. It also enables the discovery of hidden patterns, clusters of events, and outliers. We design two different metrics to preprocess the spatio-temporal for data visualization. Although we demonstrate the usefulness of our solution in privacy-preserving publishing and visualization of spatio-temporal information by using big real-life parking data from two cities, our solution can be applicable for publishing and visualizing spatio-temporal information from many other big data." @default.
• W4206781581 created "2022-01-26" @default.
• W4206781581 creator A5053225136 @default.
• W4206781581 creator A5058776629 @default.
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• W4206781581 date "2021-12-15" @default.
• W4206781581 modified "2023-10-18" @default.
• W4206781581 title "Privacy-Preserving Publishing and Visualization of Spatial-Temporal Information" @default.
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• W4206781581 doi "https://doi.org/10.1109/bigdata52589.2021.9671564" @default.
• W4206781581 hasPublicationYear "2021" @default.
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