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16-07-2019 | Regular Paper | Issue 5/2019

The VLDB Journal 5/2019

Efficient two-dimensional Haar\(^+\) synopsis construction for the maximum absolute error measure

Journal:
The VLDB Journal > Issue 5/2019
Authors:
Jinhyun Kim, Jun-Ki Min, Kyuseok Shim
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Abstract

Several wavelet synopsis construction algorithms were previously proposed for optimal Haar\(^+\) synopses. Recently, we proposed the OptExtHP-EB algorithm to find an optimal one-dimensional \(\hbox {Haar}^+\) synopsis. By utilizing the novel properties of optimal synopses, OptExtHP-EB represents the set of optimal synopses in a node of a \(\hbox {Haar}^+\) tree by a set of extended synopses. While it is much faster than the previous \(\hbox {Haar}^+\) synopsis construction algorithms, it can handle only one-dimensional data. In this paper, we propose the OptExtHP-EB2D algorithm for two-dimensional \(\hbox {Haar}^+\) synopses by extending OptExtHP-EB. While a one-dimensional \(\hbox {Haar}^+\) tree has only two child nodes and three coefficients in a node, a two-dimensional \(\hbox {Haar}^+\) tree is much more complex in that it has four child nodes and seven coefficients per node. Thus, for each possible subset of the coefficients selected in a node, we develop the efficient methods to compute a set of optimal synopses denoted by extended synopses. Our experiments confirm the effectiveness of our proposed OptExtHP-EB2D algorithm.

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