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AdaptWID: An Adaptive, Memory-Efficient Window Aggregation Implementation
November/December 2008 (vol. 12 no. 6)
pp. 22-29
Jin Li, Portland State University
Kristin Tufte, Portland State University
David Maier, Portland State University
Vassilis Papadimos, Microsoft
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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Index Terms:
databases, query processing, data stream management
Citation:
Jin Li, Kristin Tufte, David Maier, Vassilis Papadimos, "AdaptWID: An Adaptive, Memory-Efficient Window Aggregation Implementation," IEEE Internet Computing, vol. 12, no. 6, pp. 22-29, Nov.-Dec. 2008, doi:10.1109/MIC.2008.116
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