BVAGQ-AR for Fragmented Database Replication Management

Abstract

Large amounts of data have been produced at a rapid rate since the invention of computers. This condition is the key motivation for up-to-date and forthcoming research frontiers. Replication is one of the mechanisms for managing data, since it improves data accessibility and reliability in the distributed database environment. In recent years, the amount of various data grows rapidly with widely available lowcost technology. Although we have been packed with data, we still have lacked of knowledge. Nevertheless, if the impractical data is used in database replication, this will cause waste of data storage and the time taken for a replication process will be delayed. This paper proposes Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) algorithm in order to handle fragmented database synchronous replication. BVAGQ-AR algorithm is capable for partitioning the database into disjoint fragments. Fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. Managing fragmented database replication becomes a concern for the administrator because the distributed database is disseminated into split replica partitions. The result from the experiment shows that handling fragmented database synchronous replication through proposed BVAGQ-AR algorithm able to preserve data consistency in distributed environment.

Students' Performance Prediction in Higher Education Using Multi-Agent Framework-Based Distributed Data Mining Approach

“…The current work is compared with other prominent classifiers to check the model's accuracy, and the acquired result showed that this is a promising classification tool (Al-Obeidat et al, 2018;Pérez and Kholod, 2020). Noraziah et al, (2021) the proposed algorithm "Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQAR)" to classify the data and manage fragmented database synchronous replication. Classify and fragment techniques improved the performance of the distributed database system, increased data accessibility, and reduced the transfer cost and access time.…”

“…The result of the experiment indicates that handling fragmented database synchronous replication by the proposed BVAGQ-AR algorithm is capable of maintaining data consistency in a distributed en-environment. The limitation of the work is that BVAGQ-AR does not support handling fragmented database replication transaction management by considering failure cases, and BVAGQAR has to deal with fragmented database failure cases and fault tolerance (Noraziah et al, 2021) Kiu & Ching-Chieh, (2018) in this paper, researchers used four supervised educational data mining techniques, Na¨ıve Bayesian, Multilayer Perceptron, Random Forest, Decision Tree J48, and Na¨ıve Bayesian, to predict students' especially mathematic performance in secondary school. This study indicated student social activities and background and significantly predicted student performance from the obtained results.…”

Ecological and Confined Domain Ontology Construction Scheme Using Concept Clustering for Knowledge Management

“…The operative goal of an ontology-based knowledge management system in a distributed processing environment is to improve the performance and the effectiveness of computational intelligence [42]. A major requirement of knowledge management systems is that they provide a reliable service of relevant and accurate data retrieval and availability through the implementation of various techniques and algorithms regarding computational intelligence [33,43].…”

An automate failure recovery for synchronous distributed database system

“…When the single database server is crashed or mishap happens, everything will be lost. Thus, to increase and preserve the data availability and reliability, it is better to store the data in multiple servers rather than in one server [1]- [3]. This method is called distributed database system [1], [4], [5].…”

“…Thus, to increase and preserve the data availability and reliability, it is better to store the data in multiple servers rather than in one server [1]- [3]. This method is called distributed database system [1], [4], [5]. Hence, in the event of disaster, data availability is ensured because distributed systems have higher reliability and incremental growth [6].…”