Time-decaying aggregates in out-of-order streams
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sliding-window top-k queries on uncertain streams
Proceedings of the VLDB Endowment
Finding frequent items in data streams
Proceedings of the VLDB Endowment
Methods for finding frequent items in data streams
The VLDB Journal — The International Journal on Very Large Data Bases
Sliding-window top-k queries on uncertain streams
The VLDB Journal — The International Journal on Very Large Data Bases
Handling ER-topk query on uncertain streams
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
MOA-TweetReader: real-time analysis in Twitter streaming data
DS'11 Proceedings of the 14th international conference on Discovery science
From chatter to headlines: harnessing the real-time web for personalized news recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
Efficient trade-off between speed processing and accuracy in summarizing data streams
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
FGIT'12 Proceedings of the 4th international conference on Future Generation Information Technology
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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.