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Optimizing continuous queries using update propagation with varying granularities

: Behrend, A.; Griefahn, U.; Voigt, H.; Schmiegelt, P.


Association for Computing Machinery -ACM-:
27th International Conference on Scientific and Statistical Database Management, SSDBM 2015. Proceedings : La Jolla, California, June 29 - July 01, 2015
New York: ACM, 2015
ISBN: 978-1-4503-3709-0
Art. 14
International Conference on Scientific and Statistical Database Management (SSDBM) <27, 2015, La Jolla/Calif.>
Fraunhofer FKIE ()

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 up-dates with different degrees of granularity which corresponds to an incremental query evaluation with different levels of accuracy. We show how propagation rules for different up-date 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 cut-ting down on the number of pipelined tuples considerably.