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A Model Design of Big Data Processing System using Hace Theorem

Anthony Otuonye I. Ph. D; Udunwa Ikenna A.; Nwokonkwo Obi C. Ph. D
In this research paper, we have developed a new big data processing model using the HACE theorem to fully harness the potential benefits of the big data revolution and to enhance socio-economic development of in developing countries. The paper proposes a three-tier data mining structure for big data storage, processing and analysis from a single platform and provides accurate and relevant social sensing feedback for a better understanding of our society in real-time. The whole essence of data mining is to analytically explore data in search of consistent patterns and to further validate the findings by applying the detected patterns to new data sets. Big Data concern large-volume, complex, and growing data sets with multiple, autonomous sources. Our data-driven model involves a demand-driven aggregation of information sources, mining and analysis to overcome the perceived challenges of the big data. The study became necessary due to the growing need to assist governments and business agencies to take advantage of the big data technology for the desired turn-around in their socio-economic activities. The researchers adopted the HACE theorem in the model design which characterizes the unique features of the big data revolution. The Hadoop’s MapReduce technique was also adopted for big data mining, while the k-means and Naïve Bayes algorithm was used to ensure big data clustering and classification. By this model, the suggestions of various IT scholars and authors has been achieved who observed the need to revisit most of our data mining techniques and suggested distributed versions available methods of data analysis due to the new challenges of big data
Select Volume / Issues:
Year:
2018
Type of Publication:
Article
Keywords:
Big Data; HACE Theorem; Data Mining; Open Source; Real Time
Journal:
IJECCE
Volume:
9
Number:
1
Pages:
35-41
Month:
January
ISSN:
2249-071X
Hits: 226

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