Name
Title | ||
|---|---|---|
Extract and Maintain the Most Helpful Wavelet Coefficients for Continuous K-Nearest Neighbor Queries in Stream Processing |
Abstract | ||
|---|---|---|
In the real-time series streaming environments, such as data analysis in sensor networks, online stock analysis, video surveillance and weather forecasting, similarity search, which aims at retrieving the similarity between two or more streams, is a hot issue in the recent years. How to find continuous k-nearest neighbors (CKNN) queries has been one of the most common applications in computing on DSMS. In this paper, we developed traditional skylines technique and propose W-Skyline to process CKNN queries as a bandwidth efficient approach over distributed streams. It tries to use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity search over time-series data in memory. Finally, we will give an extensive experimental study with real-time data sets that verifies the effectiveness of our W-Skyline transformation approach in similarity search and CKNN discovery within arbitrary ranges in the time series streaming environments. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14831-6_48 | ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS |
Keywords | DocType | Volume |
Continuous k-nearest neighbors (CKNN),Data stream management system (DSMS),W-Skyline algorithm,Wavelet coefficients | Conference | 93 |
ISSN | Citations | PageRank |
1865-0929 | 1 | 0.35 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Wang | 1 | 12 | 3.92 |
| Tie Hua Zhou | 2 | 4 | 3.43 |
| Ho Sun Shon | 3 | 38 | 8.00 |
| Yang Koo Lee | 4 | 44 | 8.62 |
| Keun Ho Ryu | 5 | 883 | 85.61 |