Click Through Rate Utilization Using Hadoop Framework on Cloud Environment

Authors

  • Shraddha Sharma  Department of Computer Science and Engineering, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India
  • Abhishek Kumar Dewangan  Department of Computer Science and Engineering, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India

Keywords:

Hadoop, HDFS, Hit Count, Click Throgh Document.

Abstract

In todays Internet world each one experiences the web straight forwardly or in a roundabout way. Current programming based frameworks gather data about their activity and history in click through file. These files data can be used for analysis. The click through document having timestamps, IP address, month, date, and so forth. In today's web situation click through record examination get to be distinctly vital undertaking for dissecting the client's conduct and for enhancing the web applications and managing an account frameworks, and so forth. Rapid change in the technology, make analysis of these file to predict how system will behave in an uncertain situations. Hadoop and apache spark are well known parallel processing system. HDFS and MapReduce are parallel processing systems. Click through files which are created by the web servers contain information about the activities of the visitors like number of visitors and from which domain they are visiting. Thus analyzing those file are very tedious tasks. This paper analysis wiki dataset over different nodes on AWS cloud for performance evaluation of processing of click through rate dataset over varying number of nodes as well as number of records.

References

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Published

2017-09-30

Issue

Section

Research Articles

How to Cite

[1]
Shraddha Sharma, Abhishek Kumar Dewangan, " Click Through Rate Utilization Using Hadoop Framework on Cloud Environment, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 6, pp.378-381, September-October-2017.