Efficient Generation of Approximate Region-based Geo-maps from Big Geotagged Data.

IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks(2023)

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Abstract
smart city applications scenarios, such as traffic monitoring, require regularly generating region-based geographical maps (geo-maps) such as choropleth, to uncover statistical patterns in the data, therefore helping municipalities to achieve better urban planning. However, with tremendous avalanches of big data arriving in fast streams, it is becoming cumbersome and inefficient to achieve the visualization task in a timely manner. Having said that, spatial approximate query processing presents itself as an indispensable and reliable solution in cases of data overloading. In this paper, we focus on presenting a novel system for generating efficiently region-based geo-maps from fast arriving big georeferenced data streams. We specifically present ApproxGeoViz. It is a system for generating approximate region-based maps from fast arriving data relying on a novel stratified-like spatial sampling method. We have built a standard-compliant prototype and tested its performance on real smart city data. Our results show that ApproxGeoViz is efficient in terms of time-based and accuracy-based QoS constraints such as running time and approximate map accuracy.
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