Name
Abstract | ||
|---|---|---|
Skyline queries have been actively studied lately as they can effectively identify interesting candidate objects with low formulation overhead. In particular, this paper studies supporting skyline queries for the uncertain data with "maybe" uncertainty, e.g., automatically extracted data. Prior skyline works on uncertain data assumes that every possible value for an uncertain object can be exhaustively enumerated (i.e., "alternatives" uncertainty) which is not applicable in many extraction scenarios. We develop fast algorithms that outperform the baseline approach by orders of magnitude and validate them over extensive evaluations. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICDEW.2008.4498383 | ICDE Workshops |
Keywords | Field | DocType |
database management systems,best-first search,uncertain data,tree searching,skyline ranking,r-tree,database administrator,database management system,problem trend,corrective action,skyline query ranking,automated notification,query processing,probability,data mining,arsenic,best first search,artificial neural networks,computational modeling,indium,iodine,uncertainty,object recognition,beryllium,r tree,databases | Skyline,R-tree,Data mining,Ranking,Computer science,Uncertain data,Artificial neural network,Database,Best-first search,Cognitive neuroscience of visual object recognition | Conference |
ISSN | ISBN | Citations |
1943-2895 | 978-1-4244-2162-6 | 5 |
PageRank | References | Authors |
0.45 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyountaek Yong | 1 | 17 | 0.93 |
| Jin-ha Kim | 2 | 329 | 18.78 |
| Seung-Won Hwang | 3 | 1111 | 90.50 |