scholarly journals ENHANCING PERFORMANCE AND EFFICIENCY FOR BIG DATA ANALYTICS APPLICATION IN HADOOP MAPREDUCE ENVIRONMENT

2019 ◽  
Vol 04 (08) ◽  
pp. 221-223
Author(s):  
Purushotham Naidu V ◽  
Anjan K Koundinya
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Hadoop Mapreduce

Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis

2019 ◽  
Vol 14 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Viju Raghupathi ◽  
Yilu Zhou ◽  
Wullianallur Raghupathi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Word Association ◽  
Big Data Analytics ◽  
Text Analytics ◽  
Hadoop Mapreduce ◽  
Cancer Management ◽  
Exploratory Approach ◽  
Clustering And Classification ◽  
Data Analytic

In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

Hadoop Mapreduce Framework in Big Data Analytics

2014 ◽  
Vol 8 (3) ◽  
pp. 115-119
Author(s):  
Vidyullatha Pellakuri ◽  
◽  
Dr.D. Rajeswara Rao
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mapreduce Framework ◽  
Hadoop Mapreduce

Statistical Visualization of Big Data Through Hadoop Streaming in RStudio

2022 ◽  
pp. 758-787
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Streaming ◽  
Data Set ◽  
Graphical Modeling ◽  
Hadoop Mapreduce ◽  
R Packages ◽  
Case Based

Data Visualization enables visual representation of the data set for interpretation of data in a meaningful manner from human perspective. The Statistical visualization calls for various tools, algorithms and techniques that can support and render graphical modeling. This chapter shall explore on the detailed features R and RStudio. The combination of Hadoop and R for the Big Data Analytics and its data visualization shall be demonstrated through appropriate code snippets. The integration perspective of R and Hadoop is explained in detail with the help of a utility called Hadoop streaming jar. The various R packages and their integration with Hadoop operations in the R environment are explained through suitable examples. The process of data streaming is provided using different readers of Hadoop streaming package. A case based statistical project is considered in which the data set is visualized after dual execution using the Hadoop MapReduce and R script.

Statistical Visualization of Big Data Through Hadoop Streaming in RStudio

2018 ◽  
pp. 549-577
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Streaming ◽  
Data Set ◽  
Graphical Modeling ◽  
Hadoop Mapreduce ◽  
R Packages ◽  
Case Based

Data Visualization enables visual representation of the data set for interpretation of data in a meaningful manner from human perspective. The Statistical visualization calls for various tools, algorithms and techniques that can support and render graphical modeling. This chapter shall explore on the detailed features R and RStudio. The combination of Hadoop and R for the Big Data Analytics and its data visualization shall be demonstrated through appropriate code snippets. The integration perspective of R and Hadoop is explained in detail with the help of a utility called Hadoop streaming jar. The various R packages and their integration with Hadoop operations in the R environment are explained through suitable examples. The process of data streaming is provided using different readers of Hadoop streaming package. A case based statistical project is considered in which the data set is visualized after dual execution using the Hadoop MapReduce and R script.

Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis

2022 ◽  
pp. 1843-1863
Author(s):  
Viju Raghupathi ◽  
Yilu Zhou ◽  
Wullianallur Raghupathi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Word Association ◽  
Big Data Analytics ◽  
Text Analytics ◽  
Hadoop Mapreduce ◽  
Cancer Management ◽  
Exploratory Approach ◽  
Clustering And Classification ◽  
Data Analytic

In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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