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Resume Processing using Hadoop Application

Journal: International Journal for Scientific Research and Development | IJSRD (Vol.3, No. 11)

Publication Date:

Authors : ; ; ; ;

Page : 485-488

Keywords : Big data; Hadoop; Map Reduce; HDFS; Machine Learning; Apache Tika; Parameter;

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Big data may be a gather of structured, semi-structured and unstructured data sets that contain the large amount of data, social media analytics, information management ability, period of time information. For giant data processing Hadoop uses map scale back paradigm. We have report the complete work of Hadoop Map reduce and its use for big data processing. Once explaining this system we will going to} explain however we tend to are exploitation HDFS to implement our system. We�ve used Apache Tika for process of resume and Hive is employed for data warehousing solution on prime of hadoop. Our systems are going to be providing steering to the tip users. A resume could be a kind of document utilized by human to indicate their educational background and skills. Resumes will be used for many reasons, but the most reason is employed to secure employment. A resume mainly contains a outline of job expertise and education. The resume could be a personal and educational data of worker, that an acceptable leader understands connected the duty seeker associated wont to screen candidates usually followed by an interview. Our project is deals with the parsing application developed for the resumes received through emails in various formats like Document, text etc.The thought provides associate outlook of a project on deploying data removal techniques within the method of resume info extraction into tiny and highly-structured data. The Resume computer program mechanically completely different data on the premise of various fields and parameters like name, mobile nos. etc. and large volume of resumes is not any problem for this technique and every one work is completed automatically without any personal or human involvement.

Last modified: 2016-01-28 16:53:22