Big data visualization allotting by r and python with gui tools

Big Data Visualization: Allotting by R and Python with GUI Tools Big Data Visualization: Allotting by R and Python with GUI Tools SK Ahammad Fahad Faculty of Computer and Information Technology Al-Madinah International University International Conference on Smart Computing and Electronic Enterprise Kuala Lumpur, Malaysia - July, 2018

Data Visualization The human understanding prepares perceived visual data 60,000 times responsive than text. Visible information estimates for 90 % of the instruction spread to the brain. Scanning different worksheets, spreadsheets, or reports are ordinary and wearisome at the best whereas observing charts and graphs is often sufficient easier on the eyes Visualization helps to create a sense of it all. It makes complex data more accessible, understandable and usable. Its great for particular analytical tasks, such as making comparisons or understanding. International Conference on Smart Computing and Electronic Enterprise, (ICSCEE2018) ©2018 IEEE, Kuala Lumpur, Malaysia - July, 2018

History 10th century multiple time- series graph of the changing position of the seven most prominent heavenly bodies over space and time described by Funkhouser. William Playfair’s 1821 time series graph of prices, wages, and ruling monarch over a 250 year period. International Conference on Smart Computing and Electronic Enterprise, (ICSCEE2018) ©2018 IEEE, Kuala Lumpur, Malaysia - July, 2018

GUI Tools International Conference on Smart Computing and Electronic Enterprise, (ICSCEE2018) ©2018 IEEE, Kuala Lumpur, Malaysia - July, 2018 Tools Key Feature Tableau (i) Once online, others will transfer and manipulate visualizations. (ii) Desktop application however completed graphics square measure hold on a public server. (iii) Store up to 50MB of information (with free plan) (iv) Drag-and-drop interface; no programming skills needed Infogram (i) Interactive promoting reports, sales collateral, and more. (ii) Import information, customize, and share. (iii) Simply shareable dashboards that visually track business. (iv) Mapmaker to publish professional-quality interactive maps. (v) Tremendous bank of photos and icons for Facebook, Instagram, and Twitter. ChartBlocks (i) Spreadsheets, databases, even live feeds. Import information from anyplace. (ii) Chart building wizard to select the proper information. (iii) Control virtually every facet. (iv) Grab the embed code to place chart on website or share it instantly. Datawrapper (i) Charts text doesn’t become too tiny, fewer labels seem, the color key changes its position. (ii) Create charts quick, simple. (iii) No coding or design skills. No installation needed. (iv) Charts become interactive. Bars or map areas to ascertain the underlying values and perceive the chart higher. (v) Fonts, colors, and spacing that precisely utilized in the actual newsroom, and support team can produce a chart vogue only for the client. Plotly (i) DEVELOPERS: Python, R & Shiny, MATLAB, Javascript (ii) DATA SCIENCE: Dash, Plotly.js, Plotly.py, Plotly.R (iii) BUSINESS INTELLIGENCE: Chart Studio, Dashboards, Slide Decks, Falcon SQL consumer (Free) RAW (i) As easy as a copy-paste, No worries, information is safe. (ii) Conventional and unconventional layouts. (iii) Understand and map visually your information dimensions, Visual feedback, at once. (iv) Semi-Finished vectors and information structures. Visual.ly (i) Started quickly, collaborate directly, flexible to grow. (ii) Start with a strategy, integrated product, and services. (iii) Specialized creative professionals, modify quality.

GUI Tools International Conference on Smart Computing and Electronic Enterprise, (ICSCEE2018) ©2018 IEEE, Kuala Lumpur, Malaysia - July, 2018 Tools Cost Tableau (i) Public Edition – Free (ii)Personal Edition – $999/user (iii)Professional Edition – $1,999/user Infogram (i) Basic – Free; (ii)Pro - $19/month; (iii)Business - $67/month; (iv)Team - $149/month; (v)Enterprise - Contact for resolution ChartBlocks (i)Basic – Free; (ii)Personal - $8/month; (iii)Professional - $20/month; (iv)Elite - $65/month Datawrapper (i)Single 10k – free; (ii)Single Flat - 29€/month; (iii)Team - 129€/month; (iv)Custom - 279€/month; (v)Enterprise - 879€+ Plotly (i) Cloud: STUDENT: $59/year; PERSONAL: $396/year; PROFESSIONAL: $948/year (ii) ON-PREMISES: $9,950/year, 5 User License; ON-PREMISES+DASH $15,950/year, 5 User License (iii) Plotly: COMMUNITY: (free); PERSONAL: $396/year; PROFESSIONAL: $948/year RAW Free Visual.ly Contact for quota

Refarences International Conference on Smart Computing and Electronic Enterprise, (ICSCEE2018) ©2018 IEEE, Kuala Lumpur, Malaysia - July, 2018 1. H. Jagadish, J. Gehrke, A. Labrinidis, Y. Papakonstantinou, J. Patel, R. Ramakrishnan and C. Shahabi, "Big data and its technical challenges", Communications of the ACM, vol. 57, no. 7, pp. 86-94, 2014. 2. D. Keim, H. Qu and K. Ma, "Big-Data Visualization", IEEE Computer Graphics and Applications, vol. 33, no. 4, pp. 20-21, 2013. 3. M. Mauri, T. Elli, G. Caviglia, G. Uboldi and M. Azzi, "RAWGraphs", Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter - CHItaly '17, 2017. 4. W. Yafooz, S. Abidin, N. Omar and S. Hilles, "Interactive Big Data Visualization Model Based on Hot Issues (Online News Articles)", Communications in Computer and Information Science, pp. 89-99, 2016. 5. M. Mani and S. Fei, "Effective Big Data Visualization", Proceedings of the 21st International Database Engineering & Applications Symposium on - IDEAS 2017, 2017. 6. M. FRAMPTON, COMPLETE GUIDE TO OPEN SOURCE BIG DATA STACK. [S.l.]: APRESS, 2017, pp. 295-337. 7. S. Prabhakar and L. Maves, "Big Data Analytics and Visualization: Finance", in Big Data and Visual Analytics, C. Sang and A. Thomas, Ed. Springer, Cham, 2017, pp. 219-229. 8. M. Friendly, S. Dray, H. Wickham, J. Hanley, D. Murphy and P. Li, "HistData: Data Sets from the History of Statistics and Data Visualization [R package HistData version 0.8-2]", Universidad de Costa Rica, 2018. [Online]. Available: http://mirrors.ucr.ac.cr/CRAN/web/packages/HistData/. [Accessed: 03- Mar- 2018]. 9. C. Adams, Learning Python data visualization. Birmingham, England: Packt Publishing, 2014. 10. P. Murrell, R graphics. Boca Raton: CRC Press, 2016. 11. C. Ekstrøm, The R primer, 2nd ed. Boca Raton: Chapman & Hall/CRC, 2017. 12. H. Wickham and C. Sievert, Ggplot2:Elegant Graphics for Data Analysis, 2nd ed. [Cham]: Springer, 2016. 13. B. Granger and J. VanderPlas, "Altair:Declarative Visualization in Python", Altair 1.3.0.dev0 documentation, 2016. [Online]. Available: https://altair-viz.github.io/index.html. [Accessed: 03- Mar- 2018]. 14. M. Waskom, "seaborn: statistical data visualization", seaborn 0.8.1 documentation, 2017. [Online]. Available: https://seaborn.pydata.org/. [Accessed: 03- Mar- 2018]. 15. S. Bird, L. Canavan, M. Mari, M. Paprocki, P. Rudiger, C. Tang and B. Van de Ven, "Bokeh: Python library for interactive visualization", Bokeh 0.12.14 documentation, 2015. [Online]. Available: https://bokeh.pydata.org/en/latest/. [Accessed: 03- Mar- 20118].

Big data visualization allotting by r and python with gui tools

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