Print Email Facebook Twitter Stream Window Aggregation Semantics and Optimization Title Stream Window Aggregation Semantics and Optimization Author Carbone, Paris (KTH Royal Institute of Technology) Katsifodimos, A (TU Delft Web Information Systems) Haridi, Seif (KTH Royal Institute of Technology) Contributor Sakr, Sherif (editor) Zomaya, Albert (editor) Date 2018 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 To reference this document use: http://resolver.tudelft.nl/uuid:08f37e09-8466-42c8-bfa1-8e7e5f957470 DOI https://doi.org/10.1007/978-3-319-63962-8_154-1 Publisher Springer, Cham ISBN 978-3-319-63962-8 Source Encyclopedia of Big Data Technologies Part of collection Institutional Repository Document type book chapter Rights © 2018 Paris Carbone, A Katsifodimos, Seif Haridi Files PDF encyclopedia_windows.pdf 1.3 MB Close viewer /islandora/object/uuid:08f37e09-8466-42c8-bfa1-8e7e5f957470/datastream/OBJ/view