Name
Abstract | ||
|---|---|---|
Data compression techniques such as null suppression and dictionary compression are commonly used in today's database systems. In order to effectively leverage compression, it is necessary to have the ability to efficiently and accurately estimate the size of an index if it were to be compressed. Such an analysis is critical if automated physical design tools are to be extended to handle compression. Several database systems today provide estimators for this problem based on random sampling. While this approach is efficient, there is no previous work that analyses its accuracy. In this paper, we analyse the problem of estimating the compressed size of an index from the point of view of worst-case guarantees. We show that the simple estimator implemented by several database systems has several ¿good¿ cases even though the estimator itself is agnostic to the internals of the specific compression algorithm. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICDE.2010.5447871 | Data Engineering |
Keywords | Field | DocType |
data analysis,data compression,database management systems,dictionaries,estimation theory,sampling methods,compression algorithm,data compression techniques,database systems,dictionary compression,estimators,index compression fraction estimation,null suppression,random sampling | Data mining,Algorithm design,Dictionary coder,Computer science,Capacity planning,Sampling (statistics),Estimation theory,Data compression,Estimator,Lossless compression | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-4244-5444-0 | 7 |
PageRank | References | Authors |
0.78 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stratos Idreos | 1 | 1079 | 63.03 |
| Raghav Kaushik | 2 | 7 | 0.78 |
| Vivek R. Narasayya | 3 | 7 | 0.78 |
| Ravishankar Ramamurthy | 4 | 7 | 0.78 |