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How to exploit the device diversity and database interaction to propose a generic cost model?

Published: 09 October 2013 Publication History

Abstract

Cost models have been following the life cycle of databases. In the first generation, they have been used by query optimizers, where the cost-based optimization paradigm has been developed and supported by most of important optimizers. The spectacular development of complex decision queries amplifies the interest of the physical design phase (PhD), where cost models are used to select the relevant optimization techniques such as indexes, materialized views, etc. Most of these cost models are usually developed for one storage device (usually disk) with a well identified storage model and ignore the interaction between the different components of databases: interaction between optimization techniques, interaction between queries, interaction between devices, etc. In this paper, we propose a generic cost model for the physical design that can be instantiated for each need. We contribute an ontology describing storage devices. Furthermore, we provide an instantiation of our meta model for two interdependent problems: query scheduling and buffer management. The evaluation results show the applicability of our model as well as its effectiveness.

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  • (2024)Deep variability modeling to enhance reproducibility of database performance testingCluster Computing10.1007/s10586-024-04533-027:8(11683-11708)Online publication date: 2-Jun-2024
  • (2024)Generating Custom Learned Cost Model for Query Optimizer of DBMSModel-Driven Engineering and Software Development10.1007/978-3-031-66339-0_2(29-53)Online publication date: 6-Sep-2024
  • (2021)DeepCM: Deep neural networks to improve accuracy prediction of database cost modelsConcurrency and Computation: Practice and Experience10.1002/cpe.672434:10Online publication date: 8-Dec-2021
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      cover image ACM Other conferences
      IDEAS '13: Proceedings of the 17th International Database Engineering & Applications Symposium
      October 2013
      222 pages
      ISBN:9781450320252
      DOI:10.1145/2513591
      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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      • UPC: Technical University of Catalunya
      • BytePress
      • Concordia University: Concordia University

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 09 October 2013

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      Author Tags

      1. devices
      2. generic cost model
      3. ontologies
      4. physical design

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      • Research-article

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      IDEAS '13
      Sponsor:
      • UPC
      • Concordia University

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      IDEAS '13 Paper Acceptance Rate 9 of 51 submissions, 18%;
      Overall Acceptance Rate 74 of 210 submissions, 35%

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      Cited By

      View all
      • (2024)Deep variability modeling to enhance reproducibility of database performance testingCluster Computing10.1007/s10586-024-04533-027:8(11683-11708)Online publication date: 2-Jun-2024
      • (2024)Generating Custom Learned Cost Model for Query Optimizer of DBMSModel-Driven Engineering and Software Development10.1007/978-3-031-66339-0_2(29-53)Online publication date: 6-Sep-2024
      • (2021)DeepCM: Deep neural networks to improve accuracy prediction of database cost modelsConcurrency and Computation: Practice and Experience10.1002/cpe.672434:10Online publication date: 8-Dec-2021
      • (2021)Capitalizing the database cost models process through a service‐based pipelineConcurrency and Computation: Practice and Experience10.1002/cpe.646335:11Online publication date: 11-Jul-2021
      • (2020)Moving Database Cost Models from Darkness to LightSmart Applications and Data Analysis10.1007/978-3-030-45183-7_2(17-32)Online publication date: 4-Jun-2020
      • (2017)MetricStore repositoryProceedings of the Symposium on Applied Computing10.1145/3019612.3019821(1820-1825)Online publication date: 3-Apr-2017
      • (2017)Towards an Explicitation and a Conceptualization of Cost Models in Database SystemsModel and Data Engineering10.1007/978-3-319-66854-3_17(223-231)Online publication date: 6-Sep-2017
      • (2016)A Meta-advisor Repository for Database Physical DesignModel and Data Engineering10.1007/978-3-319-45547-1_6(72-87)Online publication date: 7-Sep-2016
      • (2014)What can Emerging Hardware do for your DBMS Buffer?Proceedings of the 17th International Workshop on Data Warehousing and OLAP10.1145/2666158.2666181(91-94)Online publication date: 7-Nov-2014
      • (2013)Performance analysis and modeling of SQLite embedded databases on flash file systemsDesign Automation for Embedded Systems10.1007/s10617-014-9149-217:3-4(507-542)Online publication date: 1-Sep-2013

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