Advertisement

The Visual Side of the Data

  • Marco Angelini
  • Tiziana Catarci
  • Massimo MecellaEmail author
  • Giuseppe Santucci
Chapter
Part of the Studies in Big Data book series (SBD, volume 31)

Abstract

In the last decades, visually querying data and visualizing information have been investigated in order to allow users to get insights and extract knowledge from data. Nowadays, these functionalities should be adapted to big data, including streaming ones. In this chapter, we will review the main approaches to visual queries and provide an historical overview of information visualization.

Keywords

Big data Visual query languages Information visualization 

References

  1. 1.
    D.J. Abadi, D. Carney, U. Çetintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, S.B. Zdonik, Aurora: a new model and architecture for data stream management. VLDB J. 12(2), 120–139 (2003)CrossRefGoogle Scholar
  2. 2.
    K. Abdullah, C.P. Lee, G.J. Conti, J.A. Copeland, J.T. Stasko, Ids rainstorm: visualizing ids alarms, in VizSEC (2005), p. 1Google Scholar
  3. 3.
    B. Alsallakh, W. Aigner, S. Miksch, M.E. Gröller, Reinventing the contingency wheel: scalable visual analytics of large categorical data. IEEE Trans. Vis. Comput. Graph. 18(12), 2849–2858 (2012)CrossRefGoogle Scholar
  4. 4.
    K. Andrews, H. Heidegger, Information slices: visualising and exploring large hierarchies using cascading, semi-circular discs, in Proceedings of IEEE Infovis 98 Late Breaking Hot Topics (1998), pp. 9–11Google Scholar
  5. 5.
    M. Angelaccio, T. Catarci, G. Santucci, Qbd*: a graphical query language with recursion. IEEE Trans. Softw. Eng. 16, 1150–1163 (1990)CrossRefGoogle Scholar
  6. 6.
    M. Angelini, G. Santucci, Modeling incremental visualizations, in Proceedings of the EuroVis Workshop on Visual Analytics (EuroVA 13) (2013), pp. 13–17Google Scholar
  7. 7.
    M. Ankerst, D.A. Keim, H-P. Kriegel, Circle segments: a technique for visually exploring large multidimensional data sets, in Visualization (1996)Google Scholar
  8. 8.
    A.O. Artero, M.C.F. de Oliveira, H. Levkowitz, Uncovering clusters in crowded parallel coordinates visualizations, in IEEE Symposium on Information Visualization, 2004, INFOVIS 2004 (IEEE, 2004), pp. 81–88Google Scholar
  9. 9.
    A.N. Badre, T. Catarci, A. Massari, G. Santucci, Comparative ease of use of a diagrammatic versus an iconic query language, in Interfaces to Databases (IDS-3), Proceedings of the 3rd International Workshop on Interfaces to Databases, Napier University, Edinburgh, 8-10 July 1996 (1996)Google Scholar
  10. 10.
    N.H. Balkir, G. Özsoyoglu, Z.M. Özsoyoglu, A graphical query language: VISUAL and its query processing. IEEE Trans. Knowl. Data Eng. 14(5), 955–978 (2002)CrossRefGoogle Scholar
  11. 11.
    E. Bauleo, S. Carnevale, T. Catarci, S. Kimani, M. Leva, M. Mecella, Design, realization and user evaluation of the smartvortex visual query system for accessing data streams in industrial engineering applications. J. Vis. Lang. Comput. 25(5), 577–601 (2014)CrossRefGoogle Scholar
  12. 12.
    E. Bertini, L. Dell’Aquila, G. Santucci, Springview: cooperation of radviz and parallel coordinates for view optimization and clutter reduction, in Proceedings - Third International Conference on Coordinated and Multiple Views in Exploratory Visualization, CMV 2005, vol. 2005 (2005), pp. 22–29Google Scholar
  13. 13.
    E. Bertini, G. Santucci, By chance is not enough: preserving relative density through non uniform sampling. Proc. Intern. Conf. Inf. Vis. 8, 622–629 (2004)Google Scholar
  14. 14.
    E. Bertini, G. Santucci, Quality metrics for 2d scatterplot graphics: Automatically reducing visual clutter. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3031, 77–89 (2004)Google Scholar
  15. 15.
    S.P. Callahan, L. Bavoil, V. Pascucci, C.T. Silva, Progressive volume rendering of large unstructured grids. IEEE Trans. Vis. Comput. Graph. 12(5), 1345–1352 (2006)CrossRefGoogle Scholar
  16. 16.
    T. Catarci, M.F. Costabile, S. Levialdi, C. Batini, Visual query systems for databases: a survey. J. Vis. Lang. Comput. 8(2), 215–260 (1997)CrossRefGoogle Scholar
  17. 17.
    T. Catarci, T. Di Mascio, E. Franconi, G. Santucci, S. Tessaris, An ontology based visual tool for query formulation support. Lect. Notes Comput. Sci. 2889, 32–33 (2003)CrossRefGoogle Scholar
  18. 18.
    R. Chang, G. Wessel, R. Kosara, E. Sauda, W. Ribarsky, Legible cities: focus-dependent multi-resolution visualization of urban relationships. IEEE Trans. Visual Comput. Graph. 13(6), 1169–1175 (2007)CrossRefGoogle Scholar
  19. 19.
    J. Choo, H. Park, Customizing computational methods for visual analytics with big data. IEEE Comput. Graph. Appl. 33(4), 22–28 (2013)CrossRefGoogle Scholar
  20. 20.
    M.C. Chuah, Dynamic aggregation with circular visual designs, in Proceedings of the IEEE Symposium on Information Visualization, 1998 (IEEE, 1998), pp. 35–43Google Scholar
  21. 21.
    M. Cox, Large data management for interactive visualization design, in Proceedings of the SIGGRAPH’99 System Designs for Visualizing Large-Scale Scientific Data Course Notes (1999), pp. 5–29Google Scholar
  22. 22.
    T.A. Defanti, M.D. Brown, Visualization in scientific computing. Adv. Comput. 33, 247–307 (1991)CrossRefGoogle Scholar
  23. 23.
    J.R. Eagan Jr, M.J. Harrold, J.A. Jones, J.T. Stasko, Visually encoding program test information to find faults in software. Technical report, Georgia Institute of Technology (2001)Google Scholar
  24. 24.
    D. Ellsworth, B. Green, P. Moran, Interactive terascale particle visualization, in Proceedings of the Conference on Visualization’04 (IEEE Computer Society, 2004), pp. 353–360Google Scholar
  25. 25.
    N. Elmqvist, J. Stasko, P. Tsigas, Datameadow: a visual canvas for analysis of large-scale multivariate data. Inf. Vis. 7(1), 18–33 (2008)CrossRefGoogle Scholar
  26. 26.
    J-D. Fekete, C. Plaisant, Interactive information visualization of a million items, in IEEE Symposium on Information Visualization, 2002, INFOVIS 2002 (IEEE, 2002), pp. 117–124Google Scholar
  27. 27.
    K.A. Frenkel, The art and science of visualizing data. Commun. ACM 31(2), 111–121 (1988)CrossRefGoogle Scholar
  28. 28.
    Y-H. Fua, M.O. Ward, E.A. Rundensteiner, Hierarchical parallel coordinates for exploration of large datasets, in Proceedings of the Conference on Visualization’99: Celebrating Ten Years (IEEE Computer Society Press, 1999), pp. 43–50Google Scholar
  29. 29.
    J. Elect. Comput. Eng. Analytical review of data visualization methods in application to big data. 2013, 22 (2013)Google Scholar
  30. 30.
    D. Guo, Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans. Vis. Comput. Graph. 15(6), 1041–1048 (2009)CrossRefGoogle Scholar
  31. 31.
    S. Havre, B. Hetzler, L. Nowell, Themeriver: visualizing theme changes over time, in IEEE Symposium on Information Visualization, 2000, InfoVis 2000 (IEEE, 2000), pp. 115–123Google Scholar
  32. 32.
    J. Heer, D. Boyd, Vizster: visualizing online social networks, in IEEE Symposium on Information Visualization, 2005, INFOVIS 2005 (IEEE, 2005), pp. 32–39Google Scholar
  33. 33.
    J. Hong, D.H. Jeong, C.D. Shaw, W. Ribarsky, M. Borodovsky, C.G. Song, Gvis: a scalable visualization framework for genomic data, in EuroVis, vol. 5 (2005), pp. 191–198Google Scholar
  34. 34.
    S. Huron, R. Vuillemot, J.-D. Fekete, Visual sedimentation. IEEE Trans. Visual Comput. Graph. 19(12), 2446–2455 (2013)CrossRefGoogle Scholar
  35. 35.
    J.-F. Im, M.J. McGuffin, R. Leung, Gplom: the generalized plot matrix for visualizing multidimensional multivariate data. IEEE Trans. Visual Comput. Graph. 19(12), 2606–2614 (2013)CrossRefGoogle Scholar
  36. 36.
    A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensional geometry, in Proceedings of the 1st Conference on Visualization’90 (IEEE Computer Society Press, 1990), pp. 361–378Google Scholar
  37. 37.
    Y. Jia, J. Hoberock, M. Garland, J. Hart, On the visualization of social and other scale-free networks. IEEE Trans. Visual Comput. Graph. 14(6), 1285–1292 (2008)CrossRefGoogle Scholar
  38. 38.
    B. Johnson, B. Shneiderman, Tree-maps: a space-filling approach to the visualization of hierarchical information structures, in Proceedings of the 2nd Conference on Visualization’91 (IEEE Computer Society Press, 1991), pp. 284–291Google Scholar
  39. 39.
    D.A. Keim, Pixel-oriented visualization techniques for exploring very large data bases. J. Comput. Graph. Stat. 5(1), 58–77 (1996)Google Scholar
  40. 40.
    D.A. Keim, M. Ankerst, H.-P. Kriegel, Recursive pattern: a technique for visualizing very large amounts of data, in Proceedings of the 6th Conference on Visualization’95 (IEEE Computer Society, 1995), p. 279Google Scholar
  41. 41.
    D.A. Keim, M.C. Hao, J. Ladisch, M. Hsu, U. Dayal, Pixel bar charts: a new technique for visualizing large multi-attribute data sets without aggregation, in IEEE Symposium on Information Visualization, 2001, INFOVIS 2001 (2001), pp. 113–120Google Scholar
  42. 42.
    D.A. Keim, H.-P. Kriegel, T. Seidl, Visual feedback in querying large databases, in Proceedings of the 4th Conference on Visualization’93 (IEEE Computer Society, 1993), pp. 158–165Google Scholar
  43. 43.
    D. Kenwright, Automation or interaction: what’s best for big data? in Proceedings of the Visualization’99 (IEEE, 1999), pp. 491–495Google Scholar
  44. 44.
    J. Kohlhammer, D. Keim, M. Pohl, G. Santucci, G. Andrienko, Solving problems with visual analytics. Procedia Comput. Sci- Europ. Future Technol. Conf. Exhib. 7, 117–120 (2011)CrossRefGoogle Scholar
  45. 45.
    D. Lembo, D. Pantaleone, V. Santarelli, D.F. Savo, Easy OWL drawing with the graphol visual ontology language, in Principles of Knowledge Representation and Reasoning: Proceedings of the Fifteenth International Conference, KR 2016, Cape Town, South Africa, 25–29 April, 2016 (2016), pp. 573–576Google Scholar
  46. 46.
    A. Lex, H.-J. Schulz, M. Streit, C. Partl, D. Schmalstieg, Visbricks: multiform visualization of large, inhomogeneous data. IEEE Trans. Visual Comput. Graph. 17(12), 2291–2300 (2011)CrossRefGoogle Scholar
  47. 47.
    A. Lex, M. Streit, C. Partl, K. Kashofer, D. Schmalstieg, Comparative analysis of multidimensional, quantitative data. IEEE Trans. Visual Comput. Graph. 16(6), 1027–1035 (2010)CrossRefGoogle Scholar
  48. 48.
    F. Mansmann, D.A. Keim, S.C. North, B. Rexroad, D. Sheleheda, Visual analysis of network traffic for resource planning, interactive monitoring, and interpretation of security threats. IEEE Trans. Visual Comput. Graph. 13(6), 1105–1112 (2007)CrossRefGoogle Scholar
  49. 49.
    A. Massari, S. Pavani, L. Saladini, P.K. Chrysanthis, Qbi: query by icons (1995)Google Scholar
  50. 50.
    A.J. Morris, A.I. Abdelmoty, B.A. El-Geresy, A visual query language for large spatial databases, in Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2002, Trento, Italy, 22–24 May, 2002 (2002), pp. 359–360Google Scholar
  51. 51.
    N. Murray, N. Paton, C. Goble, Kaleidoquery: a visual query language for object databases (1998), pp. 247–257Google Scholar
  52. 52.
    E.J. Nam, Y. Han, K. Mueller, A. Zelenyuk, D. Imre, Clustersculptor: a visual analytics tool for high-dimensional data, in IEEE Symposium on Visual Analytics Science and Technology, 2007, VAST 2007 (IEEE, 2007), pp. 75–82Google Scholar
  53. 53.
    F.V. Paulovich, C.T. Silva, L.G. Nonato, Two-phase mapping for projecting massive data sets. IEEE Trans. Visual Comput. Graph. 16(6), 1281–1290 (2010)CrossRefGoogle Scholar
  54. 54.
    S. Polyviou, G. Samaras, P. Evripidou, A relationally complete visual query language for heterogeneous data sources and pervasive querying, in Proceedings of the 21st International Conference on Data Engineering, ICDE 2005, 5–8 April 2005, Tokyo, Japan (2005), pp. 471–482Google Scholar
  55. 55.
    K. Reda, A. Febretti, A. Knoll, J. Aurisano, J. Leigh, A. Johnson, M.E. Papka, M. Hereld, Visualizing large, heterogeneous data in hybrid-reality environments. IEEE Comput. Graph. Appl. 33(4), 38–48 (2013)CrossRefGoogle Scholar
  56. 56.
    S. Rinzivillo, D. Pedreschi, M. Nanni, F. Giannotti, N. Andrienko, G. Andrienko, Visually driven analysis of movement data by progressive clustering. Inf. Vis. 7(3–4), 225–239 (2008)CrossRefGoogle Scholar
  57. 57.
    S. Rose, S. Butner, W. Cowley, M. Gregory, J. Walker, Describing story evolution from dynamic information streams, in IEEE Symposium on Visual Analytics Science and Technology, 2009, VAST 2009 (IEEE, 2009), pp. 99–106Google Scholar
  58. 58.
    S.J. Rysavy, D. Bromley, V. Daggett, Dive: a graph-based visual-analytics framework for big data. IEEE Comput. Graph. Appl. 34(2), 26–37 (2014)CrossRefGoogle Scholar
  59. 59.
    G. Santucci, P.A. Sottile, Query by diagram: a visual environment for querying databases. Softw. Pract. Exp., 23(3), 317–340 (1993), cited By 12Google Scholar
  60. 60.
    H.-J. Schulz, M. Angelini, G. Santucci, H. Schumann, An enhanced visualization process model for incremental visualization. IEEE Trans. Visual Comput. Graph. 22(7), 1830–1842 (2016)CrossRefGoogle Scholar
  61. 61.
    B. Shneiderman, The eyes have it: a task by data type taxonomy for information visualizations, in Proceedings of the IEEE Symposium on Visual Languages, 1996 (IEEE, 1996), pp. 336–343Google Scholar
  62. 62.
    A. Soylu, M. Giese, R. Schlatte, E. Jiménez-Ruiz, E. Kharlamov, Ö.L. Özçep, C. Neuenstadt, S. Brandt, Querying industrial stream-temporal data: an ontology-based visual approach\({}^{\text{1 }}\). JAISE 9(1), 77–95 (2017)Google Scholar
  63. 63.
    S.P. Reiss, A visual query language for software visualization (2002), pp. 80–82Google Scholar
  64. 64.
    C. Stolte, D. Tang, P. Hanrahan, Polaris: a system for query, analysis, and visualization of multidimensional relational databases. IEEE Trans. Visual Comput. Graph. 8(1), 52–65 (2002)CrossRefGoogle Scholar
  65. 65.
    C. Stolte, D. Tang, P. Hanrahan, Multiscale visualization using data cubes. IEEE Trans. Visual Comput. Graph. 9(2), 176–187 (2003)CrossRefGoogle Scholar
  66. 66.
    S.T. Teoh, K.L. Ma, S.F. Wu, X. Zhao, Case study: interactive visualization for internet security, in Proceedings of the Conference on Visualization’02 (IEEE Computer Society, 2002), pp. 505–508Google Scholar
  67. 67.
    S. Van den Elzen, J.J. Van Wijk, Multivariate network exploration and presentation: from detail to overview via selections and aggregations. IEEE Trans. Visual Comput. Graph. 20(12), 2310–2319 (2014)CrossRefGoogle Scholar
  68. 68.
    J.J. van Wijk, H.J. Spoelder, W.-J. Knibbe, K.E. Shahroudi, Interactive exploration and modeling of large data sets: a case study with venus light scattering data, in Proceedings of the 7th Conference on Visualization’96 (IEEE Computer Society Press, 1996), pp. 433–ffGoogle Scholar
  69. 69.
    C. Weaver, Building highly-coordinated visualizations in improvise, in IEEE Symposium on Information Visualization, 2004, INFOVIS 2004 (IEEE, 2004), pp. 159–166Google Scholar
  70. 70.
    C. Weaver, Multidimensional visual analysis using cross-filtered views, in IEEE Symposium on Visual Analytics Science and Technology, 2008, VAST’08 (IEEE, 2008), pp. 163–170Google Scholar
  71. 71.
    M. Weber, M. Alexa, W. Müller, Visualizing time-series on spirals. Infovis 1, 7–14 (2001)Google Scholar
  72. 72.
    N. Wong, S. Carpendale, S. Greenberg, Edgelens: an interactive method for managing edge congestion in graphs, in IEEE Symposium on Information Visualization, 2003, INFOVIS 2003 (IEEE, 2003), pp. 51–58Google Scholar
  73. 73.
    P.C. Wong, H. Foote, D. Adams, W. Cowley, J. Thomas, Dynamic visualization of transient data streams, in IEEE Symposium on Information Visualization, 2003, INFOVIS 2003 (IEEE, 2003), pp. 97–104Google Scholar
  74. 74.
    J. Zhang, M.L. Huang, 5ws model for big data analysis and visualization, in 2013 IEEE 16th International Conference on Computational Science and Engineering (CSE) (IEEE, 2013), pp. 1021–1028Google Scholar
  75. 75.
    L. Zhang, A. Stoffel, M. Behrisch, S. Mittelstadt, T. Schreck, R. Pompl, S. Weber, H. Last, D. Keim, Visual analytics for the big data eraa comparative review of state-of-the-art commercial systems, in 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) (IEEE, 2012), pp. 173–182Google Scholar
  76. 76.
    M.M. Zloof, Query-by-example: a database language. IBM Syst. J. 16(4), 324–343 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Marco Angelini
    • 1
  • Tiziana Catarci
    • 1
  • Massimo Mecella
    • 1
    Email author
  • Giuseppe Santucci
    • 1
  1. 1.Dipartimento di Ingegneria Informatica Automatica e Gestionale Antonio RubertiSapienza Università di RomaRomaItaly

Personalised recommendations