Paper Info
Title
SUM-optimal histograms for approximate query processing
Abstract
In this paper, we study the problem of the SUM query approximation with histograms. We define a new kind of histogram called the SUM-optimal histogram which can provide better estimation result for the SUM queries than the traditional equi-depth and V-optimal histograms. We propose three methods for the histogram construction. The first one is a dynamic programming method, and the other two are approximate methods. We use a greedy strategy to insert separators into a histogram and use the stochastic gradient descent method to improve the accuracy of separators. The experimental results indicate that our method can provide better estimations for the SUM queries than the equi-depth and V-optimal histograms.
Year
DOI
Venue
2020
10.1007/s10115-020-01450-7
Knowledge and Information Systems
Keywords
DocType
Volume
Approximate query processing, Histogram, Big data
Journal
62
Issue
ISSN
Citations 
8
0219-1377
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Meifan Zhang101.69
Hongzhi Wang242173.72
Jianzhong Li36324.23
Hong Gao41086120.07