research-article

Optimizing continuous queries using update propagation with varying granularities

Authors:

Andreas Behrend,

Ulrike Griefahn,

Philip SchmiegeltAuthors Info & Claims

SSDBM '15: Proceedings of the 27th International Conference on Scientific and Statistical Database Management

Article No.: 14, Pages 1 - 12

https://doi.org/10.1145/2791347.2791368

Published: 29 June 2015 Publication History

Abstract

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 different 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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Cited By

Uzunbaz SAref W(2020)Shared Execution Techniques for Business Data Analytics over Big Data StreamsProceedings of the 32nd International Conference on Scientific and Statistical Database Management10.1145/3400903.3400932(1-4)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3400903.3400932
Herrmann KVoigt HBehrend ARausch JLehner WChirkova RYang JSuciu D(2017)Living in Parallel RealitiesProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064046(1101-1116)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064046

Index Terms

Optimizing continuous queries using update propagation with varying granularities
1. Information systems
  1. Data management systems
    1. Query languages
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query languages (principles)

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cover image ACM Other conferences

SSDBM '15: Proceedings of the 27th International Conference on Scientific and Statistical Database Management

June 2015

390 pages

ISBN:9781450337090

DOI:10.1145/2791347

Editors:
Amarnath Gupta
University of California San Diego
,
Susan Rathbun
University of California San Diego

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 29 June 2015

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SSDBM 2015

SSDBM 2015: International Conference on Scientific and Statistical Database Management

June 29 - July 1, 2015

California, La Jolla

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Cited By

Uzunbaz SAref W(2020)Shared Execution Techniques for Business Data Analytics over Big Data StreamsProceedings of the 32nd International Conference on Scientific and Statistical Database Management10.1145/3400903.3400932(1-4)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3400903.3400932
Herrmann KVoigt HBehrend ARausch JLehner WChirkova RYang JSuciu D(2017)Living in Parallel RealitiesProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064046(1101-1116)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064046

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