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ISAAC
2004
Springer

Adaptive Spatial Partitioning for Multidimensional Data Streams

9 years 9 months ago
Adaptive Spatial Partitioning for Multidimensional Data Streams
We propose a space-efficient scheme for summarizing multidimensional data streams. Our sketch can be used to solve spatial versions of several classical data stream queries efficiently. For instance, we can track ε-hotspots, which are congruent boxes containing at least an ε fraction of the stream, and maintain hierarchical heavy hitters in d dimensions. Our sketch can also be viewed as a multidimensional generalization of the ε-approximate quantile summary. The space complexity of our scheme is O(1 ε log R) if the points lie in the domain [0, R]d , where d is assumed to be a constant. The scheme extends to the sliding window model with a log(εn) factor increase in space, where n is the size of the sliding window. Our sketch can also be used to answer ε-approximate rectangular range queries over a stream of d-dimensional points.
John Hershberger, Nisheeth Shrivastava, Subhash Su
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where ISAAC
Authors John Hershberger, Nisheeth Shrivastava, Subhash Suri, Csaba D. Tóth
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