ICTACT Journal on Soft Computing

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Collaborative Approach for Trend Analysis Using Clustering Mechanisms and Big Data Technologies


Affiliations
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, India
     

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The rapid growth in technologies and social media provides us the enormous amount of data, and it opens a wider window for researchers to work on such data. One of the critical analyses of the data is to check the changing trends in data. These days, massive volumes of data are being generated and processed using Hadoop and its ecosystem tools. These tools help in fast and efficient computing of a significant amount of data. In this paper, we collaborate few popular clustering algorithms with big data technologies to analyze the usage of mobile phones and networks in various locations. We loaded and processed this dataset in Apache Hive to examine the number of users and most prominent systems in given areas, based on their location codes. Further, we compared the time taken to build the clustered model on our framework to that on Weka tool. It was observed that Weka takes comparatively longer to process the dataset. This analysis would not only help in management and segregation of a considerable amount of data but would also help mobile service providers to understand the patterns of usage by customers and network problems, which may persist in some regions.

Keywords

Big Data, Clustering Methods, Machine Learning, Hive.
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Abstract Views: 279

PDF Views: 2




  • Collaborative Approach for Trend Analysis Using Clustering Mechanisms and Big Data Technologies

Abstract Views: 279  |  PDF Views: 2

Authors

Shefali Arora
Division of Computer Engineering, Netaji Subhas Institute of Technology, India
Ruchi Mittal
Division of Computer Engineering, Netaji Subhas Institute of Technology, India
M.P.S Bhatia
Division of Computer Engineering, Netaji Subhas Institute of Technology, India

Abstract


The rapid growth in technologies and social media provides us the enormous amount of data, and it opens a wider window for researchers to work on such data. One of the critical analyses of the data is to check the changing trends in data. These days, massive volumes of data are being generated and processed using Hadoop and its ecosystem tools. These tools help in fast and efficient computing of a significant amount of data. In this paper, we collaborate few popular clustering algorithms with big data technologies to analyze the usage of mobile phones and networks in various locations. We loaded and processed this dataset in Apache Hive to examine the number of users and most prominent systems in given areas, based on their location codes. Further, we compared the time taken to build the clustered model on our framework to that on Weka tool. It was observed that Weka takes comparatively longer to process the dataset. This analysis would not only help in management and segregation of a considerable amount of data but would also help mobile service providers to understand the patterns of usage by customers and network problems, which may persist in some regions.

Keywords


Big Data, Clustering Methods, Machine Learning, Hive.

References