Stream Window Aggregation Semantics and Optimization

Book chapter (2018)

Authors

Paris Carbone KTH Royal Institute of Technology
A Katsifodimos Web Information Systems - EEMCS
Seif Haridi KTH Royal Institute of Technology
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2018 Paris Carbone, A Katsifodimos, Seif Haridi
DOI: https://doi.org/10.1007/978-3-319-63962-8_154-1
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

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