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Efficient Incremental Computation of Aggregations over Sliding Windows

Chao Zhang 1 Reza Akbarinia 2 Farouk Toumani 1
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Computing aggregation over sliding windows, i.e., finite subsets of an unbounded stream, is a core operation in streaming analytics. We propose PBA (Parallel Boundary Aggregator), a novel parallel algorithm that groups continuous slices of streaming values into chunks and exploits two buffers, cumulative slice aggregations and left cumulative slice aggregations, to compute sliding window aggregations efficiently. PBA runs in (1) time, performing at most 3 merging operations per slide while consuming () space for windows with partial aggregations. Our empirical experiments demonstrate that PBA can improve throughput up to 4× while reducing latency, compared to state-of-the-art algorithms.
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Submitted on : Thursday, September 30, 2021 - 11:26:22 AM
Last modification on : Friday, October 22, 2021 - 3:07:19 PM


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Chao Zhang, Reza Akbarinia, Farouk Toumani. Efficient Incremental Computation of Aggregations over Sliding Windows. 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD 2021), Aug 2021, Singapore, Singapore. pp.2136-2144, ⟨10.1145/3447548.3467360⟩. ⟨lirmm-03359490⟩



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