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The authors consider approximate join processing over data streams when memory limitations cause incoming tuples to overflow the available space, precluding exact processing. Selective eviction of tuples (load-shedding) is needed, but is challenging since data distributions and arrival rates are unknown a priori. Also, in many real-world applications such as for the stock market and sensor-data, different items may have different importance levels. Current methods pay little attention to load-shedding when tuples bear such importance semantics, and perform poorly due to premature tuple drops and unproductive tuple retention.
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