Advertisement

Making data visualization more efficient and effective: a survey

  • Xuedi Qin
  • Yuyu  Luo
  • Nan Tang
  • Guoliang LiEmail author
Special Issue Paper
First Online:
  • 53 Downloads

Abstract

Data visualization is crucial in today’s data-driven business world, which has been widely used for helping decision making that is closely related to major revenues of many industrial companies. However, due to the high demand of data processing w.r.t. the volume, velocity, and veracity of data, there is an emerging need for database experts to help for efficient and effective data visualization. In response to this demand, this article surveys techniques that make data visualization more efficient and effective. (1) Visualization specifications define how the users can specify their requirements for generating visualizations. (2) Efficient approaches for data visualization process the data and a given visualization specification, which then produce visualizations with the primary target to be efficient and scalable at an interactive speed. (3) Data visualization recommendation is to auto-complete an incomplete specification, or to discover more interesting visualizations based on a reference visualization.

Keywords

Data visualization Visualization languages Efficient data visualization Data visualization recommendation 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

Funding was provided by 973 Program of China (Grant No. 2015CB358700) and National Natural Science Foundation of China (Grant Nos. 61632016, 61521002, 61661166012).

References

  1. 1.
    Michael, B., Vadim, O., Jeffrey, H.: D3: Data-driven documents. TVCG 17(12), 2301–9 (2011)Google Scholar
  2. 2.
    Satyanarayan, A., Moritz, D., Wongsuphasawat, K., Heer, J.: Vega-lite: a grammar of interactive graphics. TVCG 23(1), 341–350 (2016)Google Scholar
  3. 3.
    Hanrahan, P.: Vizql: a language for query, analysis and visualization. In: SIGMOD, p. 721 (2006)Google Scholar
  4. 4.
    Tableau. https://www.tableau.com. Accessed 31 Dec 2018
  5. 5.
    Power bi: Interactive data visualization bi tools. https://powerbi.microsoft.com. Accessed 31 Dec 2018
  6. 6.
    Hyper: A hybrid oltp and olap high performance dbms. https://hyper-db.de. Accessed 31 Dec 2018
  7. 7.
    Neumann, T., Mühlbauer, T., Kemper, A.: Fast serializable multi-version concurrency control for main-memory database systems. In: SIGMOD, pp. 677–689 (2015)Google Scholar
  8. 8.
    Neumann, T.: Efficiently compiling efficient query plans for modern hardware. PVLDB 4(9), 539–550 (2011)Google Scholar
  9. 9.
    Microsoft excel. https://products.office.com/en-us/excel. Accessed 31 Dec 2018
  10. 10.
    Google sheets: Free online spreadsheets for personal use. https://www.google.com/sheets/about/. Accessed 31 Dec 2018
  11. 11.
    Oracle data visualization desktop. https://docs.oracle.com/en/middleware/bi/data-visualization-desktop/tutorials.html. Accessed 31 Dec 2018
  12. 12.
    Ibm db2. https://www.ibm.com/analytics/db2. Accessed 31 Dec 2018
  13. 13.
    Amazon quicksight: Cloud based business intelligence. https://aws.amazon.com/quicksight/. Accessed 31 Dec 2018
  14. 14.
    Vega: A visualization grammar. https://vega.github.io/vega/. Accessed 31 Dec 2018
  15. 15.
    Wickham, H.: ggplot2–elegant graphics for data analysis. J Comput. Graph. Stat. 19(1), 3–28 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Li, D., Mei, H., Shen, Y., Su, S., Zhang, W., Wang, J., Zu, M., Chen, W.: ECharts: A declarative framework for rapid construction of web-based visualization. Vis. Inform. 2, 136–146 (2018)CrossRefGoogle Scholar
  17. 17.
    Luo, Y., Qin, X., Tang, N., Li, G.: DeepEye: towards automatic data visualization. In: ICDE, pp. 101–112 (2018)Google Scholar
  18. 18.
    Siddiqui, T., Lee, J., Kim, A., Xue, E., Yu, X., Zou, S., Guo, L., Liu, C., Wang, C., Karahalios, K., Parameswaran, A.G.: Fast-forwarding to desired visualizations with zenvisage. In: CIDR (2017)Google Scholar
  19. 19.
    Kalinin, A., Cetintemel, U., Zdonik, S.: Interactive data exploration using semantic windows. In: SIGMOD, pp. 505–516 (2014)Google Scholar
  20. 20.
    Stolte, C., Hanrahan, P.: Polaris: a system for query, analysis and visualization of multi-dimensional relational databases. In: INFOVIS, pp. 5–14 (2000)Google Scholar
  21. 21.
    Qin, X., Luo, Y., Tang, N., Li, G.: DeepEye: an automatic big data visualization framework. Big Data Min. Anal. 1(1), 75–82 (2018)CrossRefGoogle Scholar
  22. 22.
    Vartak, M., Madden, S., Parameswaran, A., Polyzotis, N.: Seedb: automatically generating query visualizations. PVLDB 7(13), 1581–1584 (2014)Google Scholar
  23. 23.
    Vartak, M., Rahman, S., Madden, S., Parameswaran, A.G., Polyzotis, N.: SeeDB: efficient data-driven visualization recommendations to support visual analytics. PVLDB 8(13), 2182–2193 (2015)Google Scholar
  24. 24.
    Ding, B., Huang, S., Chaudhuri, S., Chakrabarti, K., Wang, C.: Sample + seek: approximating aggregates with distribution precision guarantee. In: SIGMOD, pp. 679–694 (2016)Google Scholar
  25. 25.
    Moritz, D., Fisher, D., Ding, B., Wang, C.: Trust, but verify: optimistic visualizations of approximate queries for exploring big data. In: CHI, pp. 2904–2915 (2017)Google Scholar
  26. 26.
    Kim, A., Blais, E., Parameswaran, A.G., Indyk, P., Madden, S., Rubinfeld, R.: Rapid sampling for visualizations with ordering guarantees. PVLDB 8(5), 521–532 (2015)Google Scholar
  27. 27.
    Fisher, D., Popov, I., Drucker, S., Schraefel, M.: Trust me, i’m partially right: incremental visualization lets analysts explore large datasets faster. In: CHI, pp. 1673–1682 (2012)Google Scholar
  28. 28.
    Rahman, S., Aliakbarpour, M., Kong, H.K., Blais, E., Karahalios, K., Parameswaran, A., Rubinfield, R., Rahman, S., Aliakbarpour, M., Kong, H.K.: I’ve seen “enough”: incrementally improving visualizations to support rapid decision making. PVLDB 10(11), 1262–1273 (2017)Google Scholar
  29. 29.
    Wesley, R.M.G., Eldridge, M., Terlecki, P.: An analytic data engine for visualization in tableau. In: SIGMOD, pp. 1185–1194 (2011)Google Scholar
  30. 30.
    Wang, Z., Ferreira, N., Wei, Y., Bhaskar, A.S., Scheidegger, C.: Gaussian cubes: real-time modeling for visual exploration of large multidimensional datasets. TVCG 23(1), 681–690 (2016)Google Scholar
  31. 31.
    Liu, Z., Jiang, B., Heer, J.: imMens: real-time visual querying of big data. In: Eurographics Conference on Visualization, pp. 421–430 (2013)CrossRefGoogle Scholar
  32. 32.
    Luo, Y., Qin, X., Tang, N., Li, G., Wang, X.: DeepEye: creating good data visualizations by keyword search. In: SIGMOD, pp. 1733–1736 (2018)Google Scholar
  33. 33.
    Wu, E., Psallidas, F., Miao, Z., Zhang, H., Rettig, L.: Combining design and performance in a data visualization management system. In: CIDR (2017)Google Scholar
  34. 34.
    Doshi, P.R., Rundensteiner, E.A., Ward, M.O.: Prefetching for visual data exploration. In: DASFAA, pp. 195–202 (2003)Google Scholar
  35. 35.
    Moritz, D., Wang, C., Nelson, G.L., Lin, H., Smith, A.M., Howe, B., Heer, J.: Formalizing visualization design knowledge as constraints: actionable and extensible models in draco. TVCG 25(1), 438–448 (2019)Google Scholar
  36. 36.
    Siddiqui, T., Kim, A., Lee, J., Karahalios, K., Parameswaran, A.G.: Effortless data exploration with zenvisage: an expressive and interactive visual analytics system. PVLDB 10(4), 457–468 (2016)Google Scholar
  37. 37.
    Qin, X., Luo, Y., Tang, N., Li, G.: DeepEye: visualizing your data by keyword search. In: EDBT Vision (2018)Google Scholar
  38. 38.
    Seo, J., Shneiderman, B.: A rank-by-feature framework for interactive exploration of multidimensional data. IV 4(2), 96–113 (2005)Google Scholar
  39. 39.
    Mackinlay, J.D., Hanrahan, P., Stolte, C.: Show me: automatic presentation for visual analysis. TVCG 13(6), 1137–1144 (2007)Google Scholar
  40. 40.
    Wang, Y., Han, F., Zhu, L., Deussen, O., Chen, B.: Line graph or scatter plot? Automatic selection of methods for visualizing trends in time series. TVCG 24(2), 1141–1154 (2018)Google Scholar
  41. 41.
    Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J.D., Howe, B., Heer, J.: Voyager: exploratory analysis via faceted browsing of visualization recommendations. TVCG 22(1), 649–658 (2016)Google Scholar
  42. 42.
    Kandel, S., Paepcke, A., Hellerstein, J., Heer, J.: Wrangler: interactive visual specification of data transformation scripts. In: CHI, pp. 3363–3372 (2011)Google Scholar
  43. 43.
    Von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J.J., Fekete, J.-D., Fellner, D.W.: Visual analysis of large graphs: state-of-the-art and future research challenges. Comput. Graph. Forum 30, 1719–1749 (2011)CrossRefGoogle Scholar
  44. 44.
    Herman, I., Melançon, G., Marshall, M.S.: Graph visualization and navigation in information visualization: a survey. TVCG 6(1), 24–43 (2000)Google Scholar
  45. 45.
    Beck, F., Burch, M., Diehl, S., Weiskopf, D.: A taxonomy and survey of dynamic graph visualization. Comput. Graph. Forum 36(1), 133–159 (2017)CrossRefGoogle Scholar
  46. 46.
    Bikakis, N., Sellis, T.: Exploration and visualization in the web of big linked data: a survey of the state of the art. arXiv preprint arXiv:1601.08059 (2016)
  47. 47.
    Marie, N., Gandon, F.: Survey of linked data based exploration systems. In: IESD (2014)Google Scholar
  48. 48.
    Dadzie, A.-S., Pietriga, E.: Visualisation of linked data-reprise. Semant. Web 8(1), 1–21 (2017)CrossRefGoogle Scholar
  49. 49.
    Katifori, A., Halatsis, C., Lepouras, G., Vassilakis, C., Giannopoulou, E.: Ontology visualization methods’a survey. ACM Comput. Surv. (CSUR) 39(4), 10 (2007)CrossRefGoogle Scholar
  50. 50.
    Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., Pascucci, V.: Visualizing high-dimensional data: advances in the past decade. TVCG 3, 1249–1268 (2017)Google Scholar
  51. 51.
    Wohlfart, E., Aigner, W., Bertone, A., Miksch, S.: Comparing information visualization tools focusing on the temporal dimensions. In: IV, pp. 69–74 (2008)Google Scholar
  52. 52.
    Mei, H., Ma, Y., Wei, Y., Chen, W.: The design space of construction tools for information visualization: A survey. J. Vis. Lang. Comput. 44, 120–132 (2018)CrossRefGoogle Scholar
  53. 53.
    Diamond, M., Mattia, A.: Data visualization: an exploratory study into the software tools used by businesses. J. Instr. Pedag. 17, 1–7 (2017)Google Scholar
  54. 54.
    Ghosh, A., Nashaat, M., Miller, J., Quader, S., Marston, C.: A comprehensive review of tools for exploratory analysis of tabular industrial datasets. Vis. Inform. 2(4), 235–253 (2018)CrossRefGoogle Scholar
  55. 55.
    Keim, D.A., Lee, J.P., Thuraisinghaman, B., Wittenbrink, C.: Database issues for data visualization: supporting interactive database exploration. In: Workshop on Database Issues for Data Visualization, pp. 12–25 (1995)Google Scholar
  56. 56.
    Idreos, S., Papaemmanouil, O., Chaudhuri, S.: Overview of data exploration techniques. In: SIGMOD, pp. 277–281 (2015)Google Scholar
  57. 57.
    Bikakis, N.: Big data visualization tools. arXiv:1801.08336 (2018)Google Scholar
  58. 58.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. TKDE 6, 734–749 (2005)Google Scholar
  59. 59.
    Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapted Interact. 12(4), 331–370 (2002)zbMATHCrossRefGoogle Scholar
  60. 60.
    Sharma, L., Gera, A.: A survey of recommendation system: research challenges. IJETT 4(5), 1989–1992 (2013)Google Scholar
  61. 61.
    Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)CrossRefGoogle Scholar
  62. 62.
    Christi, J.R., Premkumar, K.: Survey on recommendation and visualization techniques for QoS-aware web services. In: ICICES, pp. 1–6 (2014)Google Scholar
  63. 63.
    Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S., Ofek-Koifman, S.: Personalized recommendation of social software items based on social relations. In: RecSys, pp. 53–60 (2009)Google Scholar
  64. 64.
    Wei, K., Huang, J., Fu, S.: A survey of e-commerce recommender systems. In: ICSSSM, pp. 1–5 (2007)Google Scholar
  65. 65.
    Heer, J., Card, S.K., Landay, J.A.: prefuse: a toolkit for interactive information visualization. In: CHI, pp. 421–430 (2005)Google Scholar
  66. 66.
    Flare. http://flare.prefuse.org. Accessed 31 Dec 2018
  67. 67.
    Bostock, M., Heer, J.: Protovis: a graphical toolkit for visualization. TVCG 15(6), 1121–8 (2009)Google Scholar
  68. 68.
    Satyanarayan, A., Russell, R., Hoffswell, J., Heer, J.: Reactive vega: a streaming dataflow architecture for declarative interactive visualization. TVCG 22(1), 659–668 (2015)Google Scholar
  69. 69.
    Khan, M., Khan, S.S.: Data and information visualization methods, and interactive mechanisms: a survey. Int. J. Comput. Appl. 34(1), 1–14 (2011)Google Scholar
  70. 70.
    Wilkinson, L.: The Grammar of Graphics. Springer, Berlin (2005)zbMATHGoogle Scholar
  71. 71.
    Wickham, H.: A layered grammar of graphics. J. Comput. Graph. Stat. 19(1), 3–28 (2010)MathSciNetCrossRefGoogle Scholar
  72. 72.
    VanderPlas, J., Granger, B.E., Heer, J., Moritz, D., Wongsuphasawat, K., Satyanarayan, A., Lees, E., Timofeev, I., Welsh, B., Sievert, S.: Altair: interactive statistical visualizations for python. https://altair-viz.github.io. Accessed 31 Dec 2018CrossRefGoogle Scholar
  73. 73.
    Echarts. http://echarts.baidu.com. Accessed 31 Dec 2018
  74. 74.
    Shneiderman, B.: Direct manipulation: a step beyond programming languages. IEEE Comput. 16(8), 57–69 (1983)CrossRefGoogle Scholar
  75. 75.
    Qlik: Data analytics for modern business intelligence. https://www.qlik.com/us. Accessed 31 Dec 2018
  76. 76.
    Gonzalez, H., Halevy, A.Y., Jensen, C.S., Langen, A., Madhavan, J., Shapley, R., Shen, W., Goldberg-Kidon, J.: Google fusion tables: web-centered data management and collaboration. In: SIGMOD, pp. 1061–1066 (2010)Google Scholar
  77. 77.
    Ren, D., Höllerer, T., Yuan, X.: iVisDesigner: expressive interactive design of information visualizations. TVCG 20(12), 2092–2101 (2014)Google Scholar
  78. 78.
    Satyanarayan, A., Heer, J.: Lyra: An interactive visualization design environment. https://idl.cs.washington.edu/projects/lyra/. Accessed 31 Dec 2018
  79. 79.
    Yalçın, M.A., Elmqvist, N., Bederson, B.B.: Keshif: Rapid and expressive tabular data exploration for novices. TVCG 24(8), 2339–2352 (2018)Google Scholar
  80. 80.
    Liu, Z., Thompson, J., Wilson, A., Dontcheva, M., Delorey, J., Grigg, S., Kerr, B., Stasko, J.: Data illustrator. http://www.zcliu.org/di/. Accessed 31 Dec 2018
  81. 81.
    Liu, Z., Thompson, J., Wilson, A., Dontcheva, M., Delorey, J., Grigg, S., Kerr, B., Stasko, J.T.: Data illustrator: Augmenting vector design tools with lazy data binding for expressive visualization authoring. In: CHI, p. 123 (2018)Google Scholar
  82. 82.
    Warren, L.: The visual display of quantitative information. Yale J. Biol. Med. 44(4), 400–400 (1986)Google Scholar
  83. 83.
    Wongsuphasawat, K., Qu, Z., Moritz, D., Chang, R., Ouk, F., Anand, A., Mackinlay, J.D., Howe, B., Heer, J.: Voyager 2: augmenting visual analysis with partial view specifications. In: CHI, pp. 2648–2659 (2017)Google Scholar
  84. 84.
    Key, A., Howe, B., Perry, D., Aragon, C.R.: Vizdeck: self-organizing dashboards for visual analytics. In: SIGMOD, pp. 681–684 (2012)Google Scholar
  85. 85.
    Kandel, S., Parikh, R., Paepcke, A., Hellerstein, J.M., Heer, J.: Profiler: integrated statistical analysis and visualization for data quality assessment. In: AVI, pp. 547–554 (2012)Google Scholar
  86. 86.
    Elzen, S.V.D., van Wijk, J.J.: Small multiples, large singles: a new approach for visual data exploration. Comput. Graph. Forum 32(3pt2), 191–200 (2013)CrossRefGoogle Scholar
  87. 87.
    Wilkinson, L., Anand, A., Grossman, R.: Graph-theoretic scagnostics. In: IEEE Symposium on Information Visualization, 2005. IEEE, Minneapolis, MN, USA (2005)Google Scholar
  88. 88.
    Mackinlay, J.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5(2), 110–141 (1986)CrossRefGoogle Scholar
  89. 89.
    Setlur, V., Battersby, S.E., Tory, M., Gossweiler, R., Chang, A.X.: Eviza: A natural language interface for visual analysis. In: UIST, pp. 365–377 (2016)Google Scholar
  90. 90.
    Hoque, E., Setlur, V., Tory, M., Dykeman, I.: Applying pragmatics principles for interaction with visual analytics. TVCG 24(1), 309–318 (2017)Google Scholar
  91. 91.
    Wu, E., Battle, L., Madden, S.R.: The case for data visualization management systems: vision paper. PVLDB 7(10), 903–906 (2014)Google Scholar
  92. 92.
    Wu, E., Psallidas, F., Miao, Z., Zhang, H., Rettig, L., Wu, Y., Sellam, T.: Combining design and performance in a data visualization management system. In: CIDR (2017)Google Scholar
  93. 93.
    Lins, L., Klosowski, J.T., Scheidegger, C.: Nanocubes for real-time exploration of spatiotemporal datasets. TVCG 19(12), 2456–2465 (2013)Google Scholar
  94. 94.
    Pang, Z., Wu, S., Chen, G., Chen, K., Shou, L.: FlashView: an interactive visual explorer for raw data. PVLDB 10(12), 1869–1872 (2017)Google Scholar
  95. 95.
    Zoumpatianos, K., Idreos, S., Palpanas, T.: Indexing for interactive exploration of big data series. In: SIGMOD, pp. 1555–1566 (2014)Google Scholar
  96. 96.
    Piringer, H., Tominski, C., Muigg, P., Berger, W.: A multi-threading architecture to support interactive visual exploration. TVCG 15(6), 1113–1120 (2009)Google Scholar
  97. 97.
    Chan, S.-M., Xiao, L., Gerth, J., Hanrahan. P.: Maintaining interactivity while exploring massive time series. In: VAST, pp. 59–66 (2008)Google Scholar
  98. 98.
    Battle, L., Chang, R., Stonebraker, M.: Dynamic prefetching of data tiles for interactive visualization. In: SIGMOD, pp. 1363–1375 (2016)Google Scholar
  99. 99.
    Alabi, D., Wu, E.: PFunk-H: approximate query processing using perceptual models. In: HILDA@SIGMOD, pp. 10–16 (2016)Google Scholar
  100. 100.
    Bikakis, N., Papastefanatos, G., Skourla, M., Sellis, T.: A hierarchical aggregation framework for efficient multilevel visual exploration and analysis. Semant. Web 8(1), 139–179 (2017)CrossRefGoogle Scholar
  101. 101.
    Elmqvist, N., Fekete, J.D.: Hierarchical aggregation for information visualization: overview, techniques, and design guidelines. TVCG 16(3), 439–454 (2010)Google Scholar
  102. 102.
    Pahins, C.A., Stephens, S.A., Scheidegger, C., Comba, J.L.: Hashedcubes: simple, low memory, real-time visual exploration of big data. TVCG 23(1), 671–680 (2016)Google Scholar
  103. 103.
    Moritz, D., Howe, B., Heer, J.: Falcon: balancing interactive latency and resolution sensitivity for scalable linked visualizations. In: CHI, p. 694 (2019)Google Scholar
  104. 104.
    Tauheed, F., Heinis, T., Shrmann, F., Markram, H., Ailamaki, A.: SCOUT: prefetching for latent feature following queries. PVLDB 5(11), 1531–1542 (2012)Google Scholar
  105. 105.
    Yesilmurat, S.: Retrospective adaptive prefetching for interactive web gis applications. Geoinformatica 16(3), 435–466 (2012)CrossRefGoogle Scholar
  106. 106.
    Dong, H.L., Kim, J.S., Kim, S.D., Kim, K.C., Yoosung, K., Park, J.: Adaptation of a neighbor selection markov chain for prefetching tiled web GIS data. In: ADVIS, pp. 213–222 (2002)Google Scholar
  107. 107.
    Fua, Y.H., Ward, M.O., Rundensteiner, E.A.: Structure-based brushes: a mechanism for navigating hierarchically organized data and information spaces. TVCG 6(2), 150–159 (2000)Google Scholar
  108. 108.
    Tao, W., Liu, X., Demiralp, Ç., Chang, R., Stonebraker, M.: Kyrix: Interactive visual data exploration at scale. In: CIDR (2019)Google Scholar
  109. 109.
    Broy, M., Denert, E., Bayer, R., McCreight, E.: Organization and maintenance of large ordered indexes. In: Broy, M., Denert, E. (eds.) Software Pioneers. Springer, Berlin, Heidelberg (2002)CrossRefGoogle Scholar
  110. 110.
    Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J.D., Howe, B., Heer, J.: Towards a general-purpose query language for visualization recommendation. In: HILDA@SIGMOD, pp. 4–9 (2016)Google Scholar
  111. 111.
    Roth, S.F., Kolojejchick, J., Mattis, J., Goldstein, J.: Interactive graphic design using automatic presentation knowledge. In: CHI, p. 207 (1994)Google Scholar
  112. 112.
    Casner, S.M.: Task-analytic approach to the automated design of graphic presentations. ACM Trans. Graph. 10(2), 111–151 (1991)CrossRefGoogle Scholar
  113. 113.
    Bertin, J.: Semiology of graphics - diagrams, networks, maps. ESRI. ISBN: 978-1-58948-261-6. http://esripress.esri.com/display/index.cfm?fuseaction=display&websiteID=190&moduleID=0 (2010)
  114. 114.
    Cleveland, W.S., McGill, R.: Graphical perception: theory, experimentation, and application to the development of graphical methods. ASA 79(387), 531–554 (1984)Google Scholar
  115. 115.
    Shepard, R.N.: Toward a universal law of generalization for psychological science. Science 242(4880), 1317–1323 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  116. 116.
    Lewandowsky, Stephan, Spence, Ian: Discriminating strata in scatterplots. ASA 84(407), 682–688 (1989)Google Scholar
  117. 117.
    Hu, K.Z., Orghian, D., Hidalgo, C.A.: DIVE: a mixed-initiative system supporting integrated data exploration workflows. In: HILDA@SIGMOD, pp. 5:1–5:7 (2018)Google Scholar
  118. 118.
    Cleveland, W.S.: Robust locally weighted regression and smoothing scatterplots. ASA 74(368), 829–836 (1979)MathSciNetzbMATHGoogle Scholar
  119. 119.
    Silverman, B.W.: Density estimation for statistics and data analysis. Springer, pp. 1–158 (1986).  https://doi.org/10.1007/978-1-4899-3324-9 zbMATHCrossRefGoogle Scholar
  120. 120.
    Dibia, V., Demiralp, Ç.: Data2Vis: Automatic generation of data visualizations using sequence to sequence recurrent neural networks. CoRR, abs/1804.03126 (2018)Google Scholar
  121. 121.
    Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 89–96 (2005)Google Scholar
  122. 122.
    Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: ICANN, vol. 1, pp. 97–102 (2002)Google Scholar
  123. 123.
    Kim, Y., Heer, J.: Assessing effects of task and data distribution on the effectiveness of visual encodings. Comput. Graph. Forum 37(3), 157–167 (2018)CrossRefGoogle Scholar
  124. 124.
    Saket, B., Endert, A., Demiralp, C.: Task-based effectiveness of basic visualizations. TVCG PP(99), 1–1 (2017)Google Scholar
  125. 125.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  126. 126.
    Wu, Q., Burges, C.J., Svore, K.M., Gao, J.: Ranking, boosting, and model adaptation. Technical report, Microsoft Research (2008)Google Scholar
  127. 127.
    Epelbaum, T.: Deep learning: technical introduction. CoRR, arXiv:1709.01412 (2017)
  128. 128.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. NIPS 4, 3104–3112 (2014)Google Scholar
  129. 129.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Comput. Sci. arXiv preprint arXiv:1409.0473 (2014)
  130. 130.
    Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Interspeech, pp. 601–608 (2012)Google Scholar
  131. 131.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  132. 132.
    Poco, J., Heer, J.: Reverse-engineering visualizations: recovering visual encodings from chart images. Comput Graph Forum 36(3), 353–363CrossRefGoogle Scholar
  133. 133.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 159–165 (1990)zbMATHGoogle Scholar
  134. 134.
    Gotz, D., Wen, Z.: Behavior-driven visualization recommendation. In: IUI, pp. 315–324 (2009)Google Scholar
  135. 135.
    Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: World Automation Congress, pp. 158–166 (1999)Google Scholar
  136. 136.
    Liu, R.R., Jia, C.X., Zhou, T., Sun, D., Wang, B.H.: Personal recommendation via modified collaborative filtering. Physica A Stat. Mech. Appl. 388(4), 462–468 (2012)CrossRefGoogle Scholar
  137. 137.
    Soboroff, I., Nicholas, C.: Combining content and collaboration in text filtering. In: IJCAI, pp. 86–91 (1999)Google Scholar
  138. 138.
    Mutlu, B., Veas, E., Trattner, C.: VizRec: recommending personalized visualizations. TIIS 6(4), 31 (2016)CrossRefGoogle Scholar
  139. 139.
    Wu, E., Madden, S.R.: Scorpion: explaining away outliers in aggregate queries. In: PVLDB, pp. 553–564 (2013)CrossRefGoogle Scholar
  140. 140.
    Song, H., Szafir, D.A.: Where’s my data? Evaluating visualizations with missing data. IEEE Trans. Vis. Comput. Graph. 25(1), 914–924 (2019)CrossRefGoogle Scholar
  141. 141.
    Battle, L., Angelini, M., Binnig, C., Catarci, T., Eichmann, P., Fekete, J., Santucci, G., Sedlmair, M., Willett, W.: Evaluating visual data analysis systems: a discussion report. In: HILDA@SIGMOD, pp. 4:1–4:6 (2018)Google Scholar
  142. 142.
    Battle, L., Chang, R., Heer, J., Stonebraker, M.: Position statement: the case for a visualization performance benchmark. In: DSIA, pp. 1–5 (2017)Google Scholar
  143. 143.
    Jiang, L., Rahman, P., Nandi, A.: Evaluating interactive data systems: workloads, metrics, and guidelines. In: SIGMOD, pp. 1637–1644 (2018)Google Scholar
  144. 144.
    Hu, K.Z., Gaikwad, S.N.S., Hulsebos, M., Bakker, M.A., Zgraggen, E., Hidalgo, C.A., Kraska, T., Li, G., Satyanarayan, A., Demiralp, Ç.: Viznet: Towards A large-scale visualization learning and benchmarking repository. In: CHI, pp. 662 (2019)Google Scholar
  145. 145.
    Valizadegan, H., Jin, R., Zhang, R., Mao, J.: Learning to rank by optimizing NDCG measure. In: NIPS, pp. 1883–1891 (2009)Google Scholar
  146. 146.
    Rezig, E.K., Cao, L., Stonebraker, M., Simonini, G., Tao, W., Madden, S., Ouzzani, M., Tang, N., Elmagarmid, A.K.: Data civilizer 2.0: a holistic framework for data preparation and analytics. PVLDB 12(12), 1954–1957 (2019)Google Scholar
  147. 147.
    Rezig, E.K., Cao, L., Simonini, G., Schoemans, M., Madden, S., Ouzzani, M., Tang, N., Stonebraker, M.: Dagger: a data (not code) debugger. In: CIDR (2020)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Qatar Computing Research InstituteHBKUDohaQatar

Personalised recommendations

Cite article

Buy options