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.
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References
Vartak, M., Madden, S., Parameswaran, A., Polyzotis, N.: SeeDB: automatically generating query visualizations. Proc. VLDB Endow. 7(13), 1581–1584 (2014)
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)
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)
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)
https://www.postgresql.org/
https://www.splicemachine.com/
https://www.mongodb.com/
Keim, D., Qu, H., Ma, K.-L.: Big-data visualization. IEEE Comput. Graph. Appl. 33(4), 20–21 (2013)
Perry, D.B., et al.: VizDeck: streamlining exploratory visual analytics of scientific data (2013)
Fisher, D., et al.: Interactions with big data analytics. interactions 19(3), 50–59 (2012)
Wang, L., Wang, G., Alexander, C.A.: Big data and visualization: methods, challenges and technology progress. Digit. Technol. 1(1), 33–38 (2015)
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Muniswamaiah, M., Agerwala, T., Tappert, C.C. (2020). Automatic Visual Recommendation for Data Science and Analytics. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_11
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DOI: https://doi.org/10.1007/978-3-030-39442-4_11
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