Optimizing Continuous Queries Using Update Propagation with Varying Granularities

  • We investigate the possibility to use update propagation methods for optimizing the evaluation of continuous queries. Update propagation allows for the efficient determination of induced changes to derived relations resulting from an explicitly performed base table update. In order to simplify the computation process, we propose the propagation of updates with different degrees of granularity which corresponds to an incremental query evaluation with different levels of accuracy. We show how propagation rules for diferent update granularities can be systematically derived, combined and further optimized by using Magic Sets. This way, the costly evaluation of certain subqueries within a continuous query can be systematically circumvented allowing for cutting down on the number of pipelined tuples considerably.

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Metadaten
Author:Andreas Behrend, Ulrike Griefahn, Hannes Voigt, Philip Schmiegelt
DOI:https://doi.org/10.1145/2791347.2791368
Parent Title (English):Proceedings of the 27th International Conference on Scientific and Statistical Database Management
Publisher:ACM
Place of publication:New York
Editor:Amarnath Gupta, Susan Rathbun
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2021/04/22
Year of first Publication:2015
Publishing Institution:Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen
Release Date:2021/04/26
Tag:Continuous Queries; Datalog; Deductive Databases; Incremental Evaluation; Update Propagation
Pagenumber:12
First Page:Artikelnr. 14
Departments / faculties:Fachbereiche / Informatik und Kommunikation
Licence (German):License LogoEs gilt das Urheberrechtsgesetz

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