Advertisement

Use of Data Analytics for Effective E-Governance: A Case Study of “EMutation” System of Odisha

  • Pabitrananda PatnaikEmail author
  • Subhashree Pattnaik
  • Pratibha Singh
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 37)

Abstract

The e-Governance always mean Good Governance which is the delivery of services at the citizens’ end. The cost of the service is also minimum. To strengthen the service deliveries, the government needs to emphasize the Information and Communication Technology(ICT) and make best use of it. Now, the automation or process conversion from manual to computerized system is not only the objective. The use of Machine Learning, Data Mining and Artificial Intelligence are also to be applied on e-Governance to increase the throughput. Big Data and its analysis is also used for analyzing the services, their impacts on the society and sustainability of the services for the socio-economic development of the nation. Decision-making for provisioning G2G, G2B and G2C services would be accurate with these analytics. So, Data Analytics is an important stream of Computer Science to provide better governance to the society. The Land Records System of Odisha and the e-Governance initiatives taken in that area are studied in this paper. The eMutation, which is the online updation of Record of Rights (RoR) is thoroughly analyzed and the improvement of service delivery using ICT is discussed. The analysis indicates that, how e-Governance service is more effective with Data Analytics and it provides many performance indicators to the government for taking right decisions at the right time. The Dash Board, Websites and MIS reports are designed and displayed in such a manner that becomes more effective and responsive to the need of the citizens and the society.

Keywords

Data Analytics Data Mining e-Governance eMutation RoR 

Notes

Acknowledgements

We are very much thankful to the National Informatics Centre (NIC), an information technology leader in India for developing and implementing successful e-Governance applications in the country. Also, we are grateful to the Government of India as well as to the Government of Odisha for providing many useful information in different government websites.

References

  1. 1.
    Ministry of Electronics and Information Technology. http://meity.gov.in
  2. 2.
    National Informatics Centre. http://www.nic.in
  3. 3.
    Digital India Land Records Modernisation Programme. http://dilrmp.nic.in
  4. 4.
    e-Governance standardization. http://egovstandards.gov.in/
  5. 5.
    A. Ojha, e-Governance in Practice (GIFT Publishing), pp. 33–41Google Scholar
  6. 6.
  7. 7.
  8. 8.
    R. Nisbet, J. Elder, IV and gary miner. Handbook of Statistical Analysis and Data Mining Applications (Elsevier, 2009). ISBN: 978-0-12-374765-5Google Scholar
  9. 9.
    P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining (Pearson Addison Wesley, 2005), Hardcover: 769 pages. ISBN: 0321321367Google Scholar
  10. 10.
    D.J. Hand, H. Mannila, P. Smyth, Principles of Data Mining (MIT Press, Fall, 2000)Google Scholar
  11. 11.
    I. Witten, E. Frank, M. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. (Morgan Kaufmann, 2011). ISBN 978-0-12-374856-0Google Scholar
  12. 12.
    S. Chakrabarti, E. Cox, E. Frank, R.G. Ting, J. Han, X. Jiang, M. Kamber, S. Lightstone, T. Nadeau, R.E. Neapolitan, D. Pyle, M. Refaat, M. Schneider, T. Teorey, I. Witten, Data Mining: Know It All (Morgan Kaufmann, 2008)Google Scholar
  13. 13.
    R.S. Pressman, Software Engineering, A Practitioner’s Approach (McGraw Hill International Editions, 1992)Google Scholar
  14. 14.
    A. Silberschartz, H.F. Korth, S. Sudarshan, Database System Concepts, 4th edn.Google Scholar
  15. 15.
    Revenue and Disaster Management Department, Government of Odisha website. http://revenueodisha.gov.in
  16. 16.
  17. 17.
  18. 18.
  19. 19.
  20. 20.
    B. Hughes, M. Cotterell, Software Project Management, 2nd edn. (Tata McGraw-Hill, 2001), pp. 235–259Google Scholar
  21. 21.
    Government of Odisha. http://odisha.gov.in
  22. 22.
    M.W. Sholom, N. Indurkhya, Predictive Data Mining: A Practical Guide (Morgan Kaufmann, 1997)Google Scholar
  23. 23.
    K. Cios, W. Pedrycz, R. Swiniarski, L. Kurgan, Data Mining: a Knowledge Discovery Approach (Springer, 2007). ISBN: 978-0-387-33333-5Google Scholar
  24. 24.
    M. Dunham, Data Mining Introductory and Advanced Topics (Prentice Hall, 2003). ISBN: 0130888923Google Scholar
  25. 25.
    M. Berry, G. Linoff, Mastering Data Mining (Wiley, 2000)Google Scholar
  26. 26.
  27. 27.
    O.P. Rud, Data Mining Cookbook, Modeling Data for Marketing, Risk, and CRM (Wiley, 2001)Google Scholar
  28. 28.
    P. Cerrito, Introduction to Data Mining Using SAS Enterprise Miner (SAS Press, 2006). ISBN: 978-1-59047-829-5Google Scholar
  29. 29.
    S. Džeroski, A. Kobler, V. Gjorgijoski, P. Panov, Using decision trees to predict forest stand height and canopy cover from LANDSAT and LIDAR data, in 20th International Conference on Informatics for Environmental Protection—Managing Environmental Knowledge—ENVIROINFO (2006)Google Scholar
  30. 30.
    Odisha Right to Public Services Act. http://bhulekh.ori.nic.in/ORTPSA

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Pabitrananda Patnaik
    • 1
    Email author
  • Subhashree Pattnaik
    • 2
  • Pratibha Singh
    • 1
  1. 1.National Informatics CentreBhubaneswarIndia
  2. 2.Capital Institute of Management & SciencePanchagaon, BhubaneswarIndia

Personalised recommendations