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Cluster of Education and Social Sciences, Open University Malaysia
Abstract
In this investigation, he main aim was to determine how effective and feasible it would be to exploit idle computational storage. Architecturally, the proposed model was that which relied on HDFS (Hadoop Distributed File System). Also, CPN tools were used during model implementation. Hence, the tools constituted CPN ML programming language and Colored Petri Dish Nets. To ensure that the availability of the workstations was characterized within the model, the data collection process occurred in a computer lab for about 40 days. In the findings, it was established that when three tests in the form of a physical test, a cloud test, and a simulation base test are applied, the deep data locality approach yields a significant improvement in the Hadoop performance. Particularly, the use of the deep data locality technique led to a 34 percent improvement in the Hadoop system. Thus, it was concluded that the superiority of the proposed approach arises from its ability to yield a reduction in the HDFS data movement.
Rahmat, R., Musa, S., & Nordin, M. (2021). Deep Data Mining And Hadoop Simulation In Computerized Systems. Int. J. of Aquatic Science, 12(1), 44-53.
MLA
Robaisya Rahmat; ShahrulHapizah Musa; MohdNorazmi Nordin. "Deep Data Mining And Hadoop Simulation In Computerized Systems". Int. J. of Aquatic Science, 12, 1, 2021, 44-53.
HARVARD
Rahmat, R., Musa, S., Nordin, M. (2021). 'Deep Data Mining And Hadoop Simulation In Computerized Systems', Int. J. of Aquatic Science, 12(1), pp. 44-53.
VANCOUVER
Rahmat, R., Musa, S., Nordin, M. Deep Data Mining And Hadoop Simulation In Computerized Systems. Int. J. of Aquatic Science, 2021; 12(1): 44-53.
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