Skip to main content
SpringerLink
Log in
Book cover

Big Data Analytics pp 329–339Cite as

A Proposed Contextual Model for Big Data Analysis Using Advanced Analytics

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 654))

Abstract

Big Data has numerous issues related to its primary defining characteristics of the three V’s: Variety, Volume and Velocity. A greater segment of Big Data is attributed to semi-structured or unstructured text that emanates from social interactions on the web, emails, tweets, blogs, etc. Conventional approaches are overwhelmed by the data deluge and fall short to perform. These challenges consequently create scope for research in developing models to analyze data and extract actionable insights to realize the fourth V, i.e., Value. The purpose of this paper is to propose a contextual model for Resume Analytics that utilizes Semantic technologies and Analytic (Descriptive, Predictive and Prescriptive) procedures to find a befitting match between a job and candidate(s). The related work, issues and challenges and design requirements are presented along with a discussion of the analytical framework for the opted use case.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. ALEX Resume Parser: http://www.hireability.com/ALEX

  2. Abirami, A.M., Askarunisa, A., Sangeetha, R.: Ontology based ranking of documents using graph databases: a big data approach, smarter planet and big data analytics workshop. In: Co-located with International Conference on Distributed Computing and Networking, 4 Jan 2014, Amrita University, Coimbatore

    Google Scholar 

  3. Agrawal, D., et. al.: Challenges and opportunities with big data. A community white paper developed by leading researchers across the United States through Collaborative Writing between Nov 2011 to Feb 2012

    Google Scholar 

  4. Al-lasassmeh, K.Q., Kayed, A.K.A.: Latent Semantic analysis (LSA) and ontological concepts to support e-recruitment. Department of Computer Science, Faculty of Information Technology, Middle East University, June 2013

    Google Scholar 

  5. Apache Spark: https://spark.apache.org/

  6. Çelikelik, D., Elçi, A.: An ontology-based information extraction approach for Résumés. In: ICPCA-SWS 2012, LNCS 7719, pp. 165–179 (2013). Springer, Berlin

    Google Scholar 

  7. Çelikelik, D.: Towards a semantic based information extraction system for matching résumés to job openings. Computer Engineering Department, Istanbul Aydin University, Turkey

    Google Scholar 

  8. Çelikelik, D., et. al.: Towards an information extraction system based on ontology to match Résumés and jobs. In: 2013 IEEE 37th annual computer software and applications conference workshops, Kyoto, Japan, 22–26 July 2013. doi:10.1109/COMPSACW.2013.60

  9. Daxtra Intelligent Recruitment Solutions: http://www.daxtra.com

  10. Farkas, R., Dobó, A., Kurai, Z., Miklós, I., Nagy, A., Vincze, V., Zsibrita, J.: Information extraction from Hungarian, English and German CVs for a career portal. In: Prasath, R., et al. (eds.) MIKE 2014, LNAI 8891, pp. 333–341 (2014). Springer International Publishing, Switzerland

    Google Scholar 

  11. Fazel-Zarandi, M., Fox, M.S.: Semantic matchmaking for job recruitment: an ontology-based hybrid approach. In: 8th International Semantic Web Conference (2009)

    Google Scholar 

  12. Hadoop Distributed File System (HDFS): https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html

  13. Halper, F., Kaufman, M., Kirsh, D.: Text analytics: the Hurwitz victory index report. Technical Report (2013). http://www.sas.com/news/analysts/Hurwitz_Victory_Index-TextAnalytics_SAS.PDF

  14. Hitzler, P., Janowicz, K.: Linked data, big data, and the 4th Paradigm. Editorial. Semantic Web Journal by IOS Press. Feb 2013. http://www.semantic-webjournal.net/system/files/swj488.pdf

  15. Kaisler, S.H., Espinosa, J.A., Armour, F., Money, W.H.: Advanced analytics—issues and challenges in the global environment. In: 47th Hawaii international conference on system science, Hilton Waikoloa, Big Island, pp. 729–738, 6–9 Jan 2014. doi:10.1109/HICSS.2014.98

  16. Katal, A.,Wazid, M., Goudar, R.H.: Big data: issues, challenges, tools and good practices. In: Sixth International IEEE Conference on Contemporary Computing (IC3), Noida, India, pp. 349–353, 8–10 Aug 2013

    Google Scholar 

  17. Maheshwari, S., Sainani, A., Krishna Reddy, P.: An approach to extract special skills to improve the performance of resume selection. In: 6th International Workshop on Databases in Networked Information Systems (DNIS2010), Centre for Data Engineering, International Institute of Information Technology, Hyderabad, India, Mar 2010

    Google Scholar 

  18. Marjit, U., Sharma, K., Biswas, U.: Discovering resume information using linked data. Int. J. Web Semant. Technol. (IJWesT) 3(2) (2012)

    Google Scholar 

  19. Mirizzi, R., Noia, T.D., Sciascio, E.D., Michelantonio, T.: A Semantic Web enabled System for Résumé Composition and Publication. In: 3rd IEEE International Conference on Semantic Computing (ICSC 2009), Berkeley, CA, USA, 14–16 Sept 2009. doi:10.1109/ICSC.2009.40. http://www.researchgate.net/publication/221406004_A_Semantic_Web_Enabled_System_for_Rsum_Composition_and_Publication

  20. Overview of RChilli Resume Parser Copyright © 2014 RChilli Inc.: http://rchilli.com/wp-content/uploads/2014/12/Overview-of-RChilli-Resume-Parser.pdf

  21. Rajput, Q.: Ontology based semantic annotation of Urdu language web documents. In: 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2014), Gdynia, Poland, 15–17 Sept 2014, pp. 662–670. doi:10.1016/j.2014

  22. Saellstrom, P.: A system for candidate-task matching in the e-recruitment field. Umea University, Department of Computing Science, Sweden, July 2013

    Google Scholar 

  23. Sonar, S., Bankar, B.: Resume parsing with named entity clustering algorithm. http://www.slideshare.net/swapnilmsonar/resume-parsing-with-named-entity-clustering-algorithm

  24. Sovren Resume/CV Parser: http://www.sovren.com

  25. Suen, H.: The effect of end user computing competence on human resource job performance: mapping for human resource roles. Afr. J. Bus. Manage. 6(28), 8287–8295 (2012). doi:10.5897/AJBM11.577. ISSN 1993-8233 © 2012 Academic Journals. http://www.academicjournals.org/AJBM

  26. Tao, J., Deokar, A.V., El-Gayar, O.F.: An Ontology-based information extraction (OBIE) framework for analyzing initial public offering (IPO) prospectus, pp. 769–778. In: 47th Hawaii International Conference on System Science, Waikoloa, HI, USA, 6–9 Jan 2014. doi:10.1109/HICSS.2014.103

  27. Textkernel_hr_suite. Empowering Recruitment: www.textkernel.com

  28. Tinelli, E., Colucci, S., Giannini, S., Sciascio, E.D., Donini, F.M.: Large Scale Skill Matching Through Knowledge Compilation. Foundations of Intelligent Systems, Lecture Notes in Computer Science, vol. 7661, pp. 192–201 (2012). http://link.springer.com/chapter/10.1007%2F978-3-642-34624-8_23

Download references

Author information

Authors and Affiliations

  1. KLS Gogte Institute of Technology, Belagavi, India

    Manjula Ramannavar & Nandini S. Sidnal

Authors
  1. Manjula Ramannavar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nandini S. Sidnal
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Jagan Institute of Management Studies, New Delhi, Delhi, India

    V. B. Aggarwal

  2. Department of Computer Science, University of Delhi, New Delhi, Delhi, India

    Vasudha Bhatnagar

  3. Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Ramannavar, M., Sidnal, N.S. (2018). A Proposed Contextual Model for Big Data Analysis Using Advanced Analytics. In: Aggarwal, V., Bhatnagar, V., Mishra, D. (eds) Big Data Analytics. Advances in Intelligent Systems and Computing, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-10-6620-7_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6620-7_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6619-1

  • Online ISBN: 978-981-10-6620-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation