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We present a novel load shedding technique, called range loading shedding (denoted as RANGE), for sliding window joins when CPU capacity is insufficient in the system and the details of the distribution of streams are unknown. To obtain the statistics of data, we dynamically maintain clustering range histogram (CR-histogram) and average density counter table (ADC-table) for each sliding window. The CR-Histogram is constructed and maintained by clustering technique with a fixed amount of memory. When CPU capacity is insufficient, the RANGE technique is used to select tuples to be processed by utilizing the CR-Histogram and ADC-table, and then produces maximum subset join outputs. Experimental results on synthetic and real life data show that Range load shedding approach obtains max-subset results effectively, and outperforms the existing load shedding strategies.
Machine Learning and Cybernetics, 2007 International Conference on (Volume:3 )
Date of Conference: 19-22 Aug. 2007