Name
Abstract | ||
|---|---|---|
We present a novel load shedding technique over sliding window joins. We first construct a dual window architectural model including join-windows and aux-windows. With the statistics built on aux-windows, an effective load shedding strategy is developed to produce maximum subset join outputs. For the streams with high arrival rates, we propose an approach incorporating front-shedding and rear-shedding, and then address the problem of how to cooperate these two shedding processes through a series of calculations. Based on extensive experimentation with synthetic data and real life data, we show that our load shedding strategy delivers superb join output performance, and dominates the existing strategies. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11775300_40 | ADVANCES IN WEB-AGE INFORMATION MANAGEMENT, PROCEEDINGS |
Keywords | Field | DocType |
sliding window,synthetic data | Data mining,Joins,Sliding window protocol,Computer science,Real-time computing,Synthetic data,STREAMS,Architectural model,Statistical analysis,Distributed computing,Load Shedding | Conference |
Volume | ISSN | Citations |
4016 | 0302-9743 | 5 |
PageRank | References | Authors |
0.43 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Donghong Han | 1 | 70 | 8.53 |
| Chuan Xiao | 2 | 679 | 36.82 |
| Rui Zhou | 3 | 394 | 34.50 |
| Guoren Wang | 4 | 1366 | 159.46 |
| Huan Huo | 5 | 35 | 10.00 |
| Xiaoyun Hui | 6 | 16 | 2.36 |