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Automatic Visual Recommendation for Data Science and Analytics

  • Manoj Muniswamaiah
  • Tilak AgerwalaEmail author
  • Charles C. TappertEmail author
Conference paper
  • 19 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1130)

Abstract

Data visualization is used to extract insight from large datasets. Data scientists repeatedly keep generating different visualizations from the datasets for their hypothesis. Analyzing datasets which has many attributes could be a cumbersome process and lead to errors. The goal of this research paper is to automatically recommend interesting visualization patterns using optimized datasets from different databases. It reduces the time spent on low utility visualizations and displays recommended patterns.

Keywords

Big data Database Analytical query Query optimizer Data science Data visualization Data analyst 

References

  1. 1.
    Vartak, M., Madden, S., Parameswaran, A., Polyzotis, N.: SeeDB: automatically generating query visualizations. Proc. VLDB Endow. 7(13), 1581–1584 (2014)CrossRefGoogle Scholar
  2. 2.
    Johnson, A.E., Pollard, T.J., Shen, L., Li-wei, H.L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L.A., Mark, R.G.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)CrossRefGoogle Scholar
  3. 3.
    Waller, M.A., Fawcett, S.E.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logistics 34(2), 77–84 (2013)CrossRefGoogle Scholar
  4. 4.
    Muniswamaiah, M., Agerwala, T., Tappert, C.C.: Context-aware query performance optimization for big data analytics in healthcare. In: 2019 IEEE High Performance Extreme Computing Conference (HPEC-2019), pp. 1–7 (2019)Google Scholar
  5. 5.
    https://www.postgresql.org/Google Scholar
  6. 6.
    https://www.splicemachine.com/Google Scholar
  7. 7.
    https://www.mongodb.com/Google Scholar
  8. 8.
    Keim, D., Qu, H., Ma, K.-L.: Big-data visualization. IEEE Comput. Graph. Appl. 33(4), 20–21 (2013)CrossRefGoogle Scholar
  9. 9.
    Perry, D.B., et al.: VizDeck: streamlining exploratory visual analytics of scientific data (2013)Google Scholar
  10. 10.
    Fisher, D., et al.: Interactions with big data analytics. interactions 19(3), 50–59 (2012)CrossRefGoogle Scholar
  11. 11.
    Wang, L., Wang, G., Alexander, C.A.: Big data and visualization: methods, challenges and technology progress. Digit. Technol. 1(1), 33–38 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Seidenberg School of CSISPace UniversityWhite Plains, New YorkUSA

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