Exponentially Decayed Aggregates on Data Streams

In a massive stream of sequential events such as stock feeds, sensor readings, or IP traffic measurements, tuples pertaining to recent events are typically more important than older ones. It is important to compute various aggregates over such streams after applying a decay function which assigns weights to tuples based on their age. We focus on the computation of exponentially decayed aggregates in the form of quantiles and heavy hitters. Our techniques are based on extending existing data stream summaries, such as the q-digest [1] and the "space- saving" algorithm [2]. Our experiments confirm that our methods can be applied in practice, and have similar space and time costs to the non-decayed aggregate computation.