Applying Lagrange Model to Fill Data During Big Data Streaming

  • Sindhu P. MenonEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


Advancements in technology have significantly reshaped the social and economic environment. Businesses are coming up with new strategies to uncover hidden information from data in order to support better prediction and analysis. Data continues to grow at a rapid rate and it has become necessary to process the quality data. In mission critical applications, streaming of data plays a very important role. Discontinuity in data stream is unaffordable as it consumes more time and money. This paper proposes a technique through Lagrange’s Interpolation, which could avoid discontinuity in data streams when large data is being processed.


Big data Velocity Lagrange’s Interpolation Streaming 



I would like to extend my gratitude to the members of PRO-ACT who have developed the data set used in this work. PRO-ACT (Pooled Resource Open-Access ALS Clinical Trials Database) contains about 8500 records of ALS patients whose identity is hidden.


  1. 1.
    Chardonnens, T., Cudre-Mauroux, P., Grund, M., Perroud, B.: Big data analytics on high velocity streams: a case study. In: 2013 IEEE International Conference on Big Data, pp. 784–787 (2013)Google Scholar
  2. 2.
    Dupré, L., Demchenko, Y.: Impact of information security measures on the velocity of big data infrastructures. In: 2016 International Conference on High Performance Computing & Simulation (HPCS), pp. 492–500 (2016)Google Scholar
  3. 3.
  4. 4.
  5. 5.
    Tee, J.: Handling the four ‘V’s of big data: volume, velocity, variety, and veracity (2013).
  6. 6.
    Lovett, D.L., Felder, D.L.: Application of regression techniques to studies of relative growth in crustacean. J. Crustac. Biol. 9(4), 529–539 (1989)CrossRefGoogle Scholar
  7. 7.
    Manembu, P., Kewo, A., Welang, B.: Missing data solution of electricity consumption based on Lagrange Interpolation case study: IntelligEnSia data monitoring. In: International Conference on Electrical Engineering and Informatics (ICEEI), pp. 511–516 (2015)Google Scholar
  8. 8.
    Menon, S.P., Hegde, N.P.: A survey of tools and applications in big data. In: 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), pp. 1–7. IEEE (2015)Google Scholar
  9. 9.
    Sarsfield, S.: The butterfly effect of data quality. In: The Fifth MIT Information Quality Industry SymPosium (2011)Google Scholar
  10. 10.
    Sheth, A.: Transforming big data into smart data: deriving value via harnessing volume, variety, and velocity using semantic techniques and technologies. In: 2014 IEEE 30th International Conference on Data Engineering, Chicago, IL, USA, p. 2 (2014)Google Scholar
  11. 11.
    Sindhu, C.S., Hegde, N.P.: A framework to handle data heterogeneity contextual to medical big data. In: 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–7 (2015)Google Scholar
  12. 12.
    Sindhu, C.S., Hegde, N.P.: A novel integrated framework to ensure better data quality in big data analytics over cloud environment. Int. J. Electr. Comput. Eng. 7(5), 27–98 (2017)Google Scholar
  13. 13.
    Sindhu, C.S., Hegde, N.P.: An approach to mitigate veracity issue in big data using regression. Int. J. Comput. Eng. Res. (IJCER) 7(11), 51–54 (2017)Google Scholar
  14. 14.
    Eckerson, W.W.: Data quality and the bottom line: achieving business success through a commitment to high quality data. Data Warehous. Inst. 730, 1–36 (2002)Google Scholar
  15. 15.
    Williams, J.W., Aggour, K.S., Interrante, J., McHugh, J., Pool, E.: Bridging high velocity and high volume industrial big data through distributed in-memory storage & analytics. In: 2014 IEEE International Conference on Big Data (Big Data), October 27, 2014, pp. 932–941 (2014)Google Scholar
  16. 16.
    Zhang, B., Shi, Z.Z.: Classification of big velocity data via cross-domain canonical correlation analysis. In: 2013 IEEE International Conference on Big Data, pp. 493–498 (2013)Google Scholar
  17. 17.
    Zeng, D., Gu, L., Guo, S.: Cost minimization for big data processing in geo-distributed data centers. In: Cloud Networking for Big Data, pp. 59–78. Springer (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Jain College of Engineering and TechnologyHubliIndia

Personalised recommendations