Approximate voronoi cell computation on spatial data streams
The VLDB Journal — The International Journal on Very Large Data Bases
Rectangle-efficient aggregation in spatial data streams
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
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Recently there has been a growing interest in detecting patterns and analyzing trends in data that are generated continuously, often delivered in some fixed order and at a rapid rate, in the form of a data stream [5, 6]. When the stream consists of spatial data, its geometric "shape" can convey important qualitative aspects of the data set more effectively than many numerical statistics. In a stream setting, where the data must be constantly discarded and compressed, special care must be taken to ensure that the compressed summary faithfully captures the overall shape of the point distribution. We propose a novel scheme, ClusterHulls, to represent the shape of a stream of two-dimensional points. Our scheme is particularly useful when the input contains clusters with widely varying shapes and sizes, and the boundary shape, orientation, or volume of those clusters may be important in the analysis.