Name
Abstract | ||
|---|---|---|
Histogram techniques have been used in many commercial database management systems to estimate a query result size. Recently, it has been shown that they are very effective to support approximation of query processing especially aggregates. In this paper, we investigate the problem of minimizing average errors of approximate aggregates using histogram techniques. Firstly, we present a novel linear-spline histogram model that is more accurate than the existing models. Secondly, we propose a novel histogram construction technique for minimizing such average errors, which is shown to generate a near optimal histogram. Our experiment results demonstrate that the new histogram construction techniques lead to a great accuracy improvement on the existing techniques. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.datak.2006.07.009 | Data Knowl. Eng. |
Keywords | Field | DocType |
novel histogram construction technique,existing technique,existing model,average error,data reduction,query result size,new histogram construction technique,novel linear-spline histogram model,near optimal histogram,approximate range aggregate,histogram.,error minimization,query processing,techniques. keywords: approximate aggregate,histogram technique,database management system,histogram | Histogram,Data mining,Computer science,Histogram matching,Adaptive histogram equalization,Minification,Balanced histogram thresholding,Data reduction | Journal |
Volume | Issue | ISSN |
62 | 1 | 0169-023X |
Citations | PageRank | References |
1 | 0.35 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuemin Lin | 1 | 5585 | 307.32 |
| Qing Zhang | 2 | 567 | 25.85 |
| Yidong Yuan | 3 | 904 | 30.59 |
| Qing Liu | 4 | 28 | 2.19 |