research-article

HYRAQ: optimizing large-scale analytical queries through dynamic hypergraphs

Authors:

Mustapha Chaba Mouna,

Ladjel Bellatreche,

Narhimene BoustiaAuthors Info & Claims

IDEAS '20: Proceedings of the 24th Symposium on International Database Engineering & Applications

Article No.: 17, Pages 1 - 10

https://doi.org/10.1145/3410566.3410582

Published: 25 August 2020 Publication History

Abstract

In critical situations, making quick and precise decisions requires a rapid execution of a large amount of concurrent navigational and exploratory queries over collected data stored in repositories such as data warehouses. To satisfy the decision-maker's requirement, a deep understanding of the properties of these queries is necessary. In addition to their <u>large-scale</u>, they are <u>ad-hoc</u>, <u>dynamic</u> and <u>highly interacted</u>. By a quick analysis of these properties, we figure out that the first three are factual whereas the last one is behavioral. The literature has widely reported that the interaction of analytical queries has a crucial impact on selecting optimization structures (e.g., materialized views) in data storage systems. By keeping these four properties in mind, it becomes a necessity to find scalable and efficient data structures to simultaneously model them for better optimization of large-scale queries. In this paper, we first show the crucial role of the interaction phenomenon in optimizing concurrent data and mining queries by identifying its limited capacity in considering all factual properties. Secondly, we propose a dynamic hypergraph as a data structure to manage the four above properties and we show its great contribution in selecting materialized views. Finally, intensive experiments are conducted to evaluate the efficiency of our proposal and its connectivity with a commercial DBMS.

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Cited By

Mouna MBellatreche LBoustia N(2022)ProRes: Proactive re-selection of materialized viewsComputer Science and Information Systems10.2298/CSIS210606003M19:2(735-762)Online publication date: 2022
https://doi.org/10.2298/CSIS210606003M
Han YLi GYuan HSun J(2022)AutoView: An Autonomous Materialized View Management System with Encoder-ReducerIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3163195(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3163195
Mouna MBellatreche LBoustia N(2021)Selecting Subexpressions to Materialize for Dynamic Large-Scale WorkloadsBig Data Analytics and Knowledge Discovery10.1007/978-3-030-86534-4_4(39-51)Online publication date: 5-Sep-2021
https://doi.org/10.1007/978-3-030-86534-4_4

Index Terms

HYRAQ: optimizing large-scale analytical queries through dynamic hypergraphs
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
        Query optimization

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cover image ACM Other conferences

IDEAS '20: Proceedings of the 24th Symposium on International Database Engineering & Applications

August 2020

252 pages

ISBN:9781450375030

DOI:10.1145/3410566

General Chairs:
Bipin C. Desai
Concordia University, Canada
,
Wan-Sup Cho
Chungbuk National University, Korea

Copyright © 2020 ACM.

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Publication History

Published: 25 August 2020

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IDEAS 2020

IDEAS 2020: 24th International Database Engineering & Applications Symposium

August 12 - 14, 2020

Seoul, Republic of Korea

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IDEAS '20 Paper Acceptance Rate 27 of 57 submissions, 47%;

Overall Acceptance Rate 74 of 210 submissions, 35%

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Cited By

Mouna MBellatreche LBoustia N(2022)ProRes: Proactive re-selection of materialized viewsComputer Science and Information Systems10.2298/CSIS210606003M19:2(735-762)Online publication date: 2022
https://doi.org/10.2298/CSIS210606003M
Han YLi GYuan HSun J(2022)AutoView: An Autonomous Materialized View Management System with Encoder-ReducerIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3163195(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3163195
Mouna MBellatreche LBoustia N(2021)Selecting Subexpressions to Materialize for Dynamic Large-Scale WorkloadsBig Data Analytics and Knowledge Discovery10.1007/978-3-030-86534-4_4(39-51)Online publication date: 5-Sep-2021
https://doi.org/10.1007/978-3-030-86534-4_4

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