Name
Abstract | ||
|---|---|---|
Efficient handling of large volumes of data is a necessity for exascale scientific applications and database systems. To address the growing imbalance between the amount of available storage and the amount of data being produced by high speed (FLOPS) processors on the system, data must be compressed to reduce the total amount of data placed on the file systems. General-purpose loss less compression frameworks, such as zlib and bzlib2, are commonly used on datasets requiring loss less compression. Quite often, however, many scientific data sets compress poorly, referred to as hard-to-compress datasets, due to the negative impact of highly entropic content represented within the data. An important problem in better loss less data compression is to identify the hard-to-compress information and subsequently optimize the compression techniques at the byte-level. To address this challenge, we introduce the In-Situ Orthogonal Byte Aggregate Reduction Compression (ISOBAR-compress) methodology as a preconditioner of loss less compression to identify and optimize the compression efficiency and throughput of hard-to-compress datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDE.2012.114 | ICDE |
Keywords | Field | DocType |
general-purpose loss,compression technique,better loss,hard-to-compress information,hard-to-compress datasets,isobar preconditioner,data compression,compression efficiency,high-throughput lossless data compression,compression framework,scientific data,total amount,probability distribution,throughput,lossless data compression,data handling,data models,entropy,database system,data model,high throughput,flops,noise | Data mining,Data modeling,Data compression ratio,Lossy compression,Computer science,Parallel computing,Throughput,Data compression,Group method of data handling,Image compression,Database,Lossless compression | Conference |
ISSN | Citations | PageRank |
1084-4627 | 13 | 0.76 |
References | Authors | |
0 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric R. Schendel | 1 | 61 | 5.02 |
| Ye Jin | 2 | 14 | 1.12 |
| Neil Shah | 3 | 323 | 24.15 |
| Jackie Chen | 4 | 80 | 4.62 |
| C. S. Chang | 5 | 13 | 0.76 |
| Seung-Hoe Ku | 6 | 87 | 5.90 |
| Stephane Ethier | 7 | 291 | 31.10 |
| Scott Klasky | 8 | 1547 | 99.00 |
| Robert Latham | 9 | 365 | 26.39 |
| Robert Ross | 10 | 2717 | 173.13 |
| Nagiza F. Samatova | 11 | 861 | 74.04 |