Volume 8 Issue 3 - May 2017

  • 1. Distributed search engine for extraction of resume statistics using hadoop with combination of lucene indexing framework and the solr

    Authors : Geetha Guttikonda, Madhavi Katamaneni, Madhavi Latha Pandala

    Pages : 100-105

    DOI : http://dx.doi.org/10.21172/1.83.013

    Keywords : Search, Resume, Hadoop, Bigdata, Indexing, Mapreduce

    Abstract :

    Abstract- The problem most recruiters face is spending a lot of manual work for analyzing each resume/CV and searching a suitable resume. In this scenario, Resumes are located in different branches at different locations. We want to provide a single framework to search over Resume data in distributed environment. As a solution to handle big data Hadoop is the accepted framework, as it is a solution for scalable and reliable data processing workflows. The main objective is to build a distributed search engine for resume/CV data with Hadoop. For the proper storage and reporting, and to get the structured information from a large set of Resume/CV documents MapReduce (MR) a framework of Hadoop and NLP are used. The combination of Lucene indexing framework and the Solr allows us to provide search and filter functionalities for a large amount of distributed Resume data.

    Citing this Journal Article :

    Geetha Guttikonda, Madhavi Katamaneni, Madhavi Latha Pandala, "Distributed search engine for extraction of resume statistics using hadoop with combination of lucene indexing framework and the solr ", https://www.ijltet.org/journal_details.php?id=914&j_id=3735, Volume 8 Issue 3 - May 2017, 100-105, #ijltetorg