Name
Abstract | ||
|---|---|---|
An important aspect of scheduling data mining applications on Grid is the ability to accurately determine estimation of task completion time. In this paper, we present a holistic approach to estimation that uses rough sets theory to determine a similarity template and then compute a runtime estimate using identified similar task. The approach is based on frequencies of attributes appeared in discernibility matrix. Experimental result validates our hypothesis that rough sets provide an intuitively sound solution to the problem of scheduling tasks in Grid environment. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11495772_24 | AWIC |
Keywords | Field | DocType |
similar task,task completion time,knowledge grid,rough sets theory,discernibility matrix,rough set,data mining task,important aspect,holistic approach,grid environment,data mining application,rough set theory,data mining | Similitude,Data mining,Computer science,Scheduling (computing),Matrix (mathematics),Rough set,Information extraction,Artificial intelligence,Task completion,Completeness (statistics),Grid,Machine learning | Conference |
Volume | ISSN | ISBN |
3528 | 0302-9743 | 3-540-26219-9 |
Citations | PageRank | References |
5 | 0.47 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Gao | 1 | 9 | 0.90 |
| Kexiong Chen | 2 | 9 | 1.68 |
| Meiqun Liu | 3 | 9 | 1.24 |
| Jiaxun Chen | 4 | 20 | 3.71 |