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AdaptWID: An Adaptive, Memory-Efficient Window Aggregation Implementation

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4 Author(s)
Jin Li ; Portland State Univ., Portland, OR ; Tufte, K. ; Maier, D. ; Papadimos, V.

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.

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Internet Computing, IEEE  (Volume:12 ,  Issue: 6 )