Stream Window Aggregation Semantics and Optimization

Paris Carbone, Asterios Katsifodimos, Seif Haridi

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

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Abstract

Sliding windows are bounded sets which evolve together with an infinite data stream of records. Each new sliding window evicts records from the previous one while introducing newly arrived records as well. Aggregations on windows typically derive some metric such as an average or a sum of a value in each window. The main challenge of applying aggregations to sliding windows is that a naive execution can lead to a high degree of redundant computation due to a large number of common records across different windows. Special optimization techniques have been developed throughout the years to tackle redundancy and make sliding window aggregation feasible and more efficient in large data streams
Original languageEnglish
Title of host publicationEncyclopedia of Big Data Technologies
EditorsSherif Sakr, Albert Zomaya
Place of PublicationCham
PublisherSpringer
Number of pages11
ISBN (Print)978-3-319-63962-8
DOIs
Publication statusPublished - 2018

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