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Materialized views and data warehouses

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Nick RoussopoulosAuthors Info & Claims

ACM SIGMOD Record, Volume 27, Issue 1

Pages 21 - 26

https://doi.org/10.1145/273244.273253

Published: 01 March 1998 Publication History

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Abstract

A data warehouse is a redundant collection of data replicated from several possibly distributed and loosely coupled source databases, organized to answer OLAP queries. Relational views are used both as a specification technique and as an execution plan for the derivation of the warehouse data. In this position paper, we summarize the versatility of relational views and their potential.

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Prakash JVijay Kumar T(2024)A reference-point-based multi-objective materialized view selection algorithmInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02557-815:12(5676-5694)Online publication date: 27-Oct-2024
https://doi.org/10.1007/s13198-024-02557-8
Scheible RThomczyk FBlum MRautenberg MPrunotto AYazijy SBoeker M(2023)Integrating row level security in i2b2: segregation of medical records into data marts without data replication and synchronizationJAMIA Open10.1093/jamiaopen/ooad0686:3Online publication date: 14-Aug-2023
https://doi.org/10.1093/jamiaopen/ooad068
Kumar AVijay Kumar T(2022)Multi-Objective Big Data View Materialization Using Improved Strength Pareto Evolutionary AlgorithmJournal of Information Technology Research10.4018/JITR.29994715:1(1-23)Online publication date: 1-Jan-2022
https://doi.org/10.4018/JITR.299947
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Index Terms

Materialized views and data warehouses
1. Information systems
  1. Data management systems
    1. Database design and models

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OLAP queries involve a lot of aggregations on a large amount of data in data warehouses. To process expensive OLAP queries efficiently, we propose a new method to rewrite a given OLAP query using various kinds of materialized views which already exist ...
Maintaining Materialized Views for Data Warehouses with Multiple Remote Sources
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A data warehouse is a data repository which collects and maintains a large amount of data from multiple distributed, autonomous and possibly heterogeneous data sources. Often the data is stored in the form of materialized views in order to provide fast ...

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Information

Published In

ACM SIGMOD Record Volume 27, Issue 1

March 1998

103 pages

ISSN:0163-5808

DOI:10.1145/273244

Editor:
Michael Franklin
Univ. of Maryland, College Park

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 1998

Published in SIGMOD Volume 27, Issue 1

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Prakash JVijay Kumar T(2024)A reference-point-based multi-objective materialized view selection algorithmInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02557-815:12(5676-5694)Online publication date: 27-Oct-2024
https://doi.org/10.1007/s13198-024-02557-8
Scheible RThomczyk FBlum MRautenberg MPrunotto AYazijy SBoeker M(2023)Integrating row level security in i2b2: segregation of medical records into data marts without data replication and synchronizationJAMIA Open10.1093/jamiaopen/ooad0686:3Online publication date: 14-Aug-2023
https://doi.org/10.1093/jamiaopen/ooad068
Kumar AVijay Kumar T(2022)Multi-Objective Big Data View Materialization Using Improved Strength Pareto Evolutionary AlgorithmJournal of Information Technology Research10.4018/JITR.29994715:1(1-23)Online publication date: 1-Jan-2022
https://doi.org/10.4018/JITR.299947
Kumar AKumar T(2022)Multi-Objective Big Data View Materialization Using NSGA-IIIInternational Journal of Decision Support System Technology10.4018/IJDSST.31106614:1(1-28)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJDSST.311066
Kumar AKumar T(2022)Multi-Objective Big Data View Materialization Using MOGAInternational Journal of Applied Metaheuristic Computing10.4018/IJAMC.29249913:1(1-28)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJAMC.292499
Kumar AVijay Kumar T(2021)Selection of Candidate Views for Big Data View MaterializationApplications of Artificial Intelligence and Machine Learning10.1007/978-981-16-3067-5_10(113-126)Online publication date: 27-Jul-2021
https://doi.org/10.1007/978-981-16-3067-5_10
Kumar AVijay Kumar T(2020)Dynamic Web View MaterializationAdvances in Communication and Computational Technology10.1007/978-981-15-5341-7_46(605-616)Online publication date: 14-Aug-2020
https://doi.org/10.1007/978-981-15-5341-7_46
Cuzzocrea AMoussa R(2018)Towards Lambda-Based Near Real-Time OLAP over Big Data2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2018.00067(438-441)Online publication date: Jul-2018
https://doi.org/10.1109/COMPSAC.2018.00067
Cuzzocrea AMoussa RVercelli G(2018)An Innovative Lambda-Architecture-Based Data Warehouse Maintenance Framework for Effective and Efficient Near-Real-Time OLAP over Big DataBig Data – BigData 201810.1007/978-3-319-94301-5_12(149-165)Online publication date: 25-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-94301-5_12
Idris MUgarte MVansummeren SChirkova RYang JSuciu D(2017)The Dynamic Yannakakis AlgorithmProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064027(1259-1274)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064027
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Materialized views in data warehouses

Finding an efficient rewriting of OLAP queries using materialized views in data warehouses

Maintaining Materialized Views for Data Warehouses with Multiple Remote Sources

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