Skip to content
Research Article Open access CC BY 4.0

A Survey on Distributed Databases Fragmentation, Allocation and Replication Algorithms

H. A. Fuaad, A. A. Ibrahim, A. Majed, A. Asem

Current Journal of Applied Science and Technology · pp. 1–12 · Published 9 May 2018

10.9734/CJAST/2018/37079

Abstract

Due to the huge amount of computer data stored in databases, one centralized database cannot support and provide good performance and availability when contains huge data which used by large number of users. Thus, the distributed database is a good technique to overcome this problem by fragmenting the database and allocating the right database fragmentation in the right site. Many researches present static optimized algorithms of distributed database fragmentation, allocation and replication (Horizontal/ Vertical) at the initial stage of the distributed database design using different or similar techniques, which affect the performance of database system. Therefore, this study aims at reviewing and comparing the best-presented algorithms from the design perspective, with the aim of identifying the strength and weakness points of each algorithm.  Furthermore, this study could be considered as the first study that attempts to identify the most critical criteria that were used for comparing the optimized algorithms that have been proposed and used in distributed database fragmentation and allocation.

Distributed database vertical partitioning horizontal fragmentation attribute usage matrix frequency matrix attribute affinity matrix Crud matrix

Cited by 3

Gerenciamento de Migração de Dados: Um Workflow Eficiente para Empresas

Gustavo Moraes, Victor Misael, Angelo Brayner · Anais do XXXIX Simpósio Brasileiro de Banco de Dados (SBBD 2024) · 2024

Formal Approach to Data Accuracy Evaluation

Athamena Belkacem, Zina Houhamdi · Informatica · 2022

Dynamic Fragment Allocation in Distributed System with Time-Varying Parameters of its Operation

Vladimir Kulba, Sergey Somov · 2020 13th International Conference "Management of large-scale system development" (MLSD) · 2020

Article metrics

Real usage data collected on this platform.

18

Page views

0

PDF downloads

1

Outbound clicks

3

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.