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“Distributed Big Data Analysis Using Spark Parallel Data Processing” Metadata:

  • Title: ➤  Distributed Big Data Analysis Using Spark Parallel Data Processing
  • Author: ➤  

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  • Internet Archive ID: 10.11591eei.v11i3.3187

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"Distributed Big Data Analysis Using Spark Parallel Data Processing" Description:

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<span class="markedContent"><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">Nowadays, the big data marketplace is rising rapidly. The big challenge is </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">finding a system that can store and handle a huge size of data and then </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">processing that huge data for mining the hidden knowledge. This paper </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">proposed a compr</span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">ehensive system that is used for improving </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">big data </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">analysis </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">performance. It contains a fast </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">big data </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">processing engine using Apache Spark </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">and a </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">big data </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">storage environment using Apache Hadoop. The system tests </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">about 11 Gigabytes of text data which are co</span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">llected from multiple sources for </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">sentiment analysis. Three different machine learning </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr"> </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">(ML) </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr"> </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">algorithms are </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">used in this system which is already supported by the Spark </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">ML</span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr"> </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">package. The </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">system programs were written in Java and Scala programming languages and </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">the constructed model consists of the classification algorithms as well as the </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">pre</span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">-</span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">processing steps in a figure of </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr"> </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">ML</span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr"> </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr"> </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">pipeline</span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">. The proposed system was </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">implemented in both central and distributed data processing. Moreover, some </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">datasets manipulation manner</span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">s have been applied in the system tests to check </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">which manner provides the best accuracy and time performance. The results </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">showed that the system works efficiently for treating big data, it gains </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">excellent accuracy with fast execution time especially in th</span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">e distributed data </span><span style="font-size:13.7271px;font-family:sans-serif;" dir="ltr">nodes.</span></span>

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