Name
Abstract | ||
|---|---|---|
Memory efficiency is important for processing high-volume data streams. Previous stream-aggregation methods can exhibit excessive memory overhead in the presence of skewed data distributions. Further, data skew is a common feature of massive data streams. The authors introduce the AdaptWID algorithm, which uses adaptive processing to cope with time-varying data skew. AdaptWID models the memory usage of alternative aggregation algorithms and selects between them at runtime on a group-by-group basis. The authors' experimental study using the NiagaraST stream system verifies that the adaptive algorithm improves memory usage while maintaining execution cost and latency comparable to existing implementations. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/MIC.2008.116 | IEEE Internet Computing |
Keywords | Field | DocType |
adaptive memory-efficient window aggregation,adaptwid algorithm,memory usage,massive data stream,high-volume data stream processing,high-volume data stream,storage management,data stream management,memory-efficient window aggregation implementation,window id method,memory efficiency,skewed data distribution,adaptwid model,time-varying data skew,data skew,excessive memory overhead,databases,very large databases,time-varying skewed data distribution,query processing,watermarking,tin,memory management | Data processing,Data stream mining,Load balancing (computing),Computer science,Latency (engineering),Parallel computing,Adaptive memory,Memory management,Skew,Adaptive algorithm | Journal |
Volume | Issue | ISSN |
12 | 6 | 1089-7801 |
Citations | PageRank | References |
14 | 0.66 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Li | 1 | 299 | 11.91 |
| Kristin Tufte | 2 | 1241 | 146.09 |
| David Maier | 3 | 5639 | 1666.90 |
| Vassilis Papadimos | 4 | 405 | 17.65 |