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Queries over streaming data offer the potential to provide timely information for modern database applications, such as sensor networks and web services. Isoline-based visualization of streaming data has the potential to be of great use in such applications. Dynamic (real-time) isoline extraction from the streaming data is needed in order to fully harvest that potential, allowing the users to see in real time the patterns and trends - both spatial and temporal - inherent in such data. This is the goal of this paper. The authors' approach to isoline extraction is based on data terrains, Triangulated Irregular Networks (TINs) where the coordinates of the vertices corresponds to locations of data sources, and the height corresponds to their readings.
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