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An improved vertical fragmentation, allocation and replication for enhancing e‐learning in distributed database environment
Author(s) -
Sathishkumar P.,
Gunasekaran M.
Publication year - 2021
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12401
Subject(s) - replication (statistics) , computer science , cloud computing , fragmentation (computing) , distributed database , database , distributed computing , field (mathematics) , mechanism (biology) , artificial intelligence , statistics , mathematics , operating system , philosophy , epistemology , pure mathematics
Abstract E‐learning is the indispensable technique to educate huge number of people and students in short period of time with optimized usage of different kind of required resources. It is employed as a crucial teaching approach by almost all kind of educational institutions all around the world. Since e‐learning involves significant amount of resource utilization and cost, it requires some essential methodology to enhance the current system of e‐learning more efficient. The mere publication of the educational content in websites is not enough. It is very clear that, without applying suitable strategic models and concepts and establishing appropriate communication channels between contributors of e‐learning system, the educational goals cannot be achieved as we desired. Distributed database involves greater contribution in the field of cloud based e‐learning process. Basically, data replication is crucial decision of companies as database distribution can be achieved effectively by the method of database replication which generates the same copies of information called replicas. In this article, we analyze the supremacy of synergetic learning and concentrates on data replication's significance in cloud based learning system. Here we propose an excellent mechanism for data replication and enhancing the performance in terms optimized access and update of data by the determination of exact location of data through dynamic programming. The efficiency of proposed mechanism is clearly illustrated by experimental results.