Name
Abstract | ||
|---|---|---|
An increasing number of large-scale applications exploit peer-to-peer network architecture to provide highly scalable and flexible services. Among these applications, data management in peer-to-peer systems is one of the interesting domains. In this paper, we investigate the multidimensional skyline computation problem on a structured peer-to-peer network. In order to achieve low communication cost and quick response time, we utilize the iMinMax(\theta ) method to transform high-dimensional data to one-dimensional value and distribute the data in a structured peer-to-peer network called BATON. Thereafter, we propose a progressive algorithm with adaptive filter technique for efficient skyline computation in this environment. We further discuss some optimization techniques for the algorithm, and summarize the key principles of our algorithm into a query routing protocol with detailed analysis. Finally, we conduct an extensive experimental evaluation to demonstrate the efficiency of our approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TKDE.2008.235 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
detailed analysis,high-dimensional data,peer-to-peer system,multidimensional skyline computation problem,structured peer-to-peer systems,adaptive filter technique,efficient skyline computation,structured peer-to-peer network,progressive algorithm,data management,peer-to-peer network architecture,computer architecture,multidimensional systems,database management,computer networks,high dimensional data,routing protocols,adaptive filter,adaptive filters | Data structure,Peer-to-peer,Computer science,Network architecture,Adaptive filter,Adaptive algorithm,Data management,Distributed computing,Routing protocol,Scalability | Journal |
Volume | Issue | ISSN |
21 | 7 | 1041-4347 |
Citations | PageRank | References |
9 | 0.45 | 33 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Cui | 1 | 1843 | 124.59 |
| Lijiang Chen | 2 | 304 | 23.22 |
| Linhao Xu | 3 | 67 | 8.26 |
| Hua Lu | 4 | 1380 | 83.74 |
| Guojie Song | 5 | 762 | 57.31 |
| Quanqing Xu | 6 | 189 | 12.04 |