TOPSIS-based Recommender System for Big Data Visualizations
Main Article Content
Keywords
data visualization, Big Data, data visualization recommendation, TOPSIS
Abstract
Big data analytics can enable effective data interpretation that leads to highquality decision making in organizations. Since the amount of data nowadays has significantly increased in various industries, it thus motivates the research of visualizations for interdisciplinary and collaborative domains. Although there is a large collection of visualization techniques, it is very time-consuming to choose proper visualization techniques for a specific dataset.
This paper therefore aims to analyze the typical and state-of-the-art data visualization techniques for big data. Differing from traditional visualizations such as line chart or bar chart, this paper focuses on reviewing a set of modern visualizations for big data in terms of analyzing their advantages and disadvantages. To facilitate choosing a proper visualization, a recommender model based on TOPSIS is further proposed. In order to validate the proposed model, a prototype of the big data visualization recommender system has been implemented to validate the applicability of the system.
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[2] A. Alsaig, V. Alagar, O. Ormandjieva, A critical analysis of the v-model of big data, in: 2018 17th IEEE International Conference
On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And
Engineering (TrustCom/BigDataSE), IEEE, 2018, pp. 1809–1813.
[3] E. Y. Gorodov, V. V. Gubarev, Analytical review of data visualization methods in application to big data, Journal of Electrical and Computer Engineering 2013.
[4] D. Laney, 3d data management, META group research note 6 (70) (2001) 1.
[5] M. F. Uddin, N. Gupta, et al., Seven v’s of big data understanding big data to extract value, in: Proceedings of the 2014 zone 1 conference of the American Society for Engineering Education, IEEE, 2014, pp. 1–5.
[6] L. Wang, G. Wang, C. A. Alexander, Big data and visualization: methods, challenges and technology progress, Digital Technologies 1 (1) (2015) 33–38.
[7] G. Andrienko, N. Andrienko, S. Drucker, J.-D. Fekete, D. Fisher, S. Idreos, T. Kraska, G. Li, K.-L. Ma, J. Mackinlay, A. Oulasvirta, T.
Schreck, H. Schumann, M. Stonebraker, D. Auber, N. Bikakis, P. Chrysanthis, M. Sharaf, G. Papastefanatos, Big data visualization and
analytics: Future research challenges and emerging applications, in: 3rd Intl. Workshop on Big Data Visual Exploration Analytics
(BigVis 2020), 2020.
[8] M. Lebied, Misleading statistics examples – discover the potential for misuse of statistics & data in the digital age (2018).URL: https://www.datapine.com/blog/misleading-statisticsand-data
[9] R. McCready, 5 ways writers use misleading graphs to manipulate you (2020). URL: https://venngage.com/blog/misleading-graphs
[10] A. Zoss, Data visualization (2019). URL: https://guides.library.duke.edu/datavis
[11] H. R. Nagel, Scientific visualization versus infor-mation visualization, in: Workshop on state-of-the-art in scientific and parallel
computing, Sweden, 2006, pp. 8–9.
[12] TABLEAU SOFTWARE, Data visualization beginner’s guide. URL: https://www.tableau.com/learn/articles/data-visualization
[13] P. Laubheimer, Treemaps (2019). URL: https://www.nngroup.com/articles/treemaps
[14] K. Robert, Visualization research, part i (2015) URL: https://eagereyes.org/basics/visualizationresearch-engineering
[15] S. Ribecca, Sunburst diagram. URL: https://datavizcatalogue.com/methods/sunburst_diagram
[16] J. Stasko, E. Zhang, Focus + context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations, in: IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings, IEEE, 2000, pp. 57–65.
[17] Y. Holtz, Sunburst (2018). URL: https://www. data-to-viz.com/graph/sunburst
[18] K. Roden, Sequences sunburst (2020) URL: https://bl.ocks.org/kerryrodden/7090426
[19] S. Ribecca, Sankey diagram. URL: https://datavizcatalogue.com/methods/sankey_diagram
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[22] M. Schmidt, The sankey diagram in energy and material flow management: Part i: History, Journal of industrial ecology 12 (1) (2008) 82–94.
[23] Phineas, Fifa accounts – my version (2015) URL: http://www.sankey-diagrams.com/fifaaccounts-my-version
[24] Y. Holtz, Sankey diagram (2018). URL: https://www.data-to-viz.com/graph/sankey
[25] FusionCharts, Sankey diagram (2020). URL: https://www.fusioncharts.com/resources/chartprimers/sankey-diagram
[26] R. Kosara, Parallel coordinates (2010). URL: https://eagereyes.org/techniques/parallelcoordinates
[27] Y. Holtz, Parallel coordinates plot (2018). URL: https://www.data-to-viz.com/graph/parallel
[28] S. Ribecca, Parallel coordinates plot. URL: https://datavizcatalogue.com/methods/parallel_coordinates
[29] J. Johansson, C. Forsell, M. Lind, M. Cooper, Perceiving patterns in parallel coordinates: determining thresholds for identification of
relationships, Information Visualization 7 (2) (2008) 152–162.
[30] H. R. Sankey, The thermal efficiency of steamengines 125 (1896) (1896) 182–212 URL: https://doi.org/10.1680/imotp.1896.19564
[31] Z. Gemignani, Better know a visualization (2010). URL: https://www.juiceanalytics.com/writing/writing/parallel-coordinates
[32] S. Ribecca, Radar chart. URL: https://datavizcatalogue.com/methods/radar_chart
[33] M. Akmanalp, Mis-employing radar charts to distinguish multidimensional data (2018) URL: https://towardsdatascience.com/plottingin-many-dimensions-382fbd7fe76e
[34] Y. Holtz, The radar chart and its caveats (2018). URL: https://www.data-to-viz.com/caveat/spider
[35] FusionCharts, Radar chart (2020). URL: https://www.fusioncharts.com/resources/chart-primers/radar-chart
[36] G. Odds, A critique of radar charts (2011). URL: https://blog.scottlogic.com/2011/09/23/acritique-of-radar-charts
[37] S. Ribecca, Choropleth map. URL: https://datavizcatalogue.com/methods/choropleth
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[39] C. Anderson, Making choropleth maps. URL: https://www.e-education.psu.edu/geog486/node/671
[40] L. C. Rost, Choropleth maps (2018). URL: https://blog.datawrapper.de/choroplethmaps
[41] S. Ribecca, Stream graph. URL: https://datavizcatalogue.com/methods/stream_graph
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[49] Y. Holtz, Heatmap (2018). URL: https://www.data-to-viz.com/graph/heatmap
[50] A. Kirk, Heat map. URL: http://seeingdata.org/taketime/inside-the-chart-heat-map
[51] S. Ribecca, Violin plot. URL: https://datavizcatalogue.com/methods/violin_plot
[52] L. Ngo, 5 reasons you should use a violin graph (2018). URL: https://blog.bioturing.com/2018/05/16/5-reasons-you-should-use-aviolin-graph
[55] D. Coleman, Box plot with minitab (2015). URL: https://www.leansigmacorporation.com/box-plot-with-minitab
[54] Y. Holtz, Violin plot (2018). URL: https://www.data-to-viz.com/graph/violin
[55] J. Carron, Violin plots 101 (2016). URL: https://mode.com/blog/violin-plot-examples
[56] M. Yi, A complete guide to violin plots (2019). URL: https://chartio.com/learn/charts/violinplot-complete-guide
[57] H. Pampiglione, Violin plots (2019). URL: https://ion.icaew.com/data-analytics/b/blogs/posts/violin-plots
[58] Y. Holtz, Ridgeline plot (2018). URL: https://www.data-to-viz.com/graph/ridgeline
[59] T. Bodawala, Bullet charts – what is it and how to use it (2019). URL: https://www.atyantik.com/bullet-charts-what-is-it-and-how-to-use-i
[60] FusionCharts, Bullet graph (2020). URL: https://www.fusioncharts.com/resources/chart-primers/bullet-graph
[61] S. Ribecca, Bullet graph. URL: https://datavizcatalogue.com/methods/bullet_graph
[62] S. Evergreen, Bullet graph (2014). URL: https://www.betterevaluation.org/en/evaluationoptions/bullet_chart
[63] B. Singh, 5 use cases for bullet charts (2017). URL: http://visualbi.com/blogs/businessintelligence/dashboards/bullet-charts-use-cases
[64] S. Few, Bullet graph design specification, Perceptual Edge-White Paper 5.
[65] Chart-me, C14h bullet graph columns bars. URL: https://chart-me.com/portfolio-item/c14hbullet-graph-columns-bars
[66] M. Wolff, Bullet graphs. URL: https://www.chartisan.com/en/bullet-graphs
[67] Natural Resources Leadership Institute, Multicriteria decision analysis. URL: https://projects.ncsu.edu/nrli/decision-making/MCDA
[68] J. S. Dodgson, M. Spackman, A. Pearman, L. D. Phillips, Multi-criteria analysis.
[69] M. Velasquez, P. T. Hester, An analysis of multicriteria decision making methods, International journal of operations research 10 (2) (2013) 56–66.
[70] P. Bajec, D. Tuljak-Suban, A framework for detecting the proper multicriteria decisionmaking method taking into account the
characteristics of third-party logistics, the requirements of managers, and the type of input data, in: Application of Decision Science in
Business and Management,IntechOpen, 2019.
[71] C.-L. Hwang, K. Yoon, Multiple attribute decision making–methods and applications springer-verlag berlin heidelberg, New York.
[72] P. Mishra, Topsis - a multi-criteria decision making approach (2013). URL: https://www.slideshare.net/pranavmishra22/topsis-amulticriteria-decision-making-approach
[73] J. B. Cabral, N. A. Luczywo, J. L. Zanazzi, Scikit-criteria: Colecci´on de m´etodos de an´alisis multi-criterio integrado al stack cient´ıfico de Python, in: XLV Jornadas Argentinas de Inform´atica e Investigaci´on Operativa (45JAIIO)- XIV Simposio Argentino de Investigaci´on Operativa (SIO) (Buenos Aires, 2016), 2016, pp. 59–66. URL: http://45jaiio.sadio.org.ar/sites/default/ files/Sio-23.pdf
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TOPSIS-based Recommender System for Big Data Visualizations. (2023). Journal of Applied Interdisciplinary Research, 1, 50-74. https://doi.org/10.25929/jair.v1i1.114