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 we...

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Bibliographic Details
Published in2008 IEEE 24th International Conference on Data Engineering pp. 1379 - 1381
Main Authors Cormode, G., Korn, F., Tirthapura, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2008
Subjects
Aggregates
Costs
Counting circuits
Feeds
IP networks
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Summary: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.
ISBN:9781424418367
1424418364
ISSN:1063-6382
2375-026X
DOI:10.1109/ICDE.2008.4497562