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A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years pp 3–25Cite as

The Visual Side of the Data

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Part of the book series: Studies in Big Data ((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.

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Notes

  1. 1.

    cf. http://www1.unece.org/stat/platform/display/bigdata/ Classification+of+Types+of+Big+Data, accessed February 2017.

References

  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)

    Article  Google Scholar 

  2. K. Abdullah, C.P. Lee, G.J. Conti, J.A. Copeland, J.T. Stasko, Ids rainstorm: visualizing ids alarms, in VizSEC (2005), p. 1

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 9–11

    Google Scholar 

  5. M. Angelaccio, T. Catarci, G. Santucci, Qbd*: a graphical query language with recursion. IEEE Trans. Softw. Eng. 16, 1150–1163 (1990)

    Article  Google Scholar 

  6. M. Angelini, G. Santucci, Modeling incremental visualizations, in Proceedings of the EuroVis Workshop on Visual Analytics (EuroVA 13) (2013), pp. 13–17

    Google Scholar 

  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. 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. 81–88

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 22–29

    Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  19. J. Choo, H. Park, Customizing computational methods for visual analytics with big data. IEEE Comput. Graph. Appl. 33(4), 22–28 (2013)

    Article  Google Scholar 

  20. M.C. Chuah, Dynamic aggregation with circular visual designs, in Proceedings of the IEEE Symposium on Information Visualization, 1998 (IEEE, 1998), pp. 35–43

    Google Scholar 

  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. 5–29

    Google Scholar 

  22. T.A. Defanti, M.D. Brown, Visualization in scientific computing. Adv. Comput. 33, 247–307 (1991)

    Article  Google Scholar 

  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. D. Ellsworth, B. Green, P. Moran, Interactive terascale particle visualization, in Proceedings of the Conference on Visualization’04 (IEEE Computer Society, 2004), pp. 353–360

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 117–124

    Google Scholar 

  27. K.A. Frenkel, The art and science of visualizing data. Commun. ACM 31(2), 111–121 (1988)

    Article  Google Scholar 

  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. 43–50

    Google Scholar 

  29. J. Elect. Comput. Eng. Analytical review of data visualization methods in application to big data. 2013, 22 (2013)

    Google Scholar 

  30. D. Guo, Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans. Vis. Comput. Graph. 15(6), 1041–1048 (2009)

    Article  Google Scholar 

  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. 115–123

    Google Scholar 

  32. J. Heer, D. Boyd, Vizster: visualizing online social networks, in IEEE Symposium on Information Visualization, 2005, INFOVIS 2005 (IEEE, 2005), pp. 32–39

    Google Scholar 

  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. 191–198

    Google Scholar 

  34. S. Huron, R. Vuillemot, J.-D. Fekete, Visual sedimentation. IEEE Trans. Visual Comput. Graph. 19(12), 2446–2455 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 361–378

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 284–291

    Google Scholar 

  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. 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. 279

    Google Scholar 

  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. 113–120

    Google Scholar 

  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. 158–165

    Google Scholar 

  43. D. Kenwright, Automation or interaction: what’s best for big data? in Proceedings of the Visualization’99 (IEEE, 1999), pp. 491–495

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 573–576

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  49. A. Massari, S. Pavani, L. Saladini, P.K. Chrysanthis, Qbi: query by icons (1995)

    Google Scholar 

  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. 359–360

    Google Scholar 

  51. N. Murray, N. Paton, C. Goble, Kaleidoquery: a visual query language for object databases (1998), pp. 247–257

    Google Scholar 

  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. 75–82

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 471–482

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 99–106

    Google Scholar 

  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)

    Article  Google Scholar 

  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 12

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 336–343

    Google Scholar 

  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. S.P. Reiss, A visual query language for software visualization (2002), pp. 80–82

    Google Scholar 

  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)

    Article  Google Scholar 

  65. C. Stolte, D. Tang, P. Hanrahan, Multiscale visualization using data cubes. IEEE Trans. Visual Comput. Graph. 9(2), 176–187 (2003)

    Article  Google Scholar 

  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. 505–508

    Google Scholar 

  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)

    Article  Google Scholar 

  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–ff

    Google Scholar 

  69. C. Weaver, Building highly-coordinated visualizations in improvise, in IEEE Symposium on Information Visualization, 2004, INFOVIS 2004 (IEEE, 2004), pp. 159–166

    Google Scholar 

  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. 163–170

    Google Scholar 

  71. M. Weber, M. Alexa, W. Müller, Visualizing time-series on spirals. Infovis 1, 7–14 (2001)

    Google Scholar 

  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. 51–58

    Google Scholar 

  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. 97–104

    Google Scholar 

  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. 1021–1028

    Google Scholar 

  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. 173–182

    Google Scholar 

  76. M.M. Zloof, Query-by-example: a database language. IBM Syst. J. 16(4), 324–343 (1997)

    Article  Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Ingegneria Informatica Automatica e Gestionale Antonio Ruberti, Sapienza Università di Roma, via Ariosto 25, 00185, Roma, Italy

    Marco Angelini, Tiziana Catarci, Massimo Mecella & Giuseppe Santucci

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  1. Marco Angelini
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  2. Tiziana Catarci
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  3. Massimo Mecella
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  4. Giuseppe Santucci
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Correspondence to Massimo Mecella .

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Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Sergio Flesca

  2. University of Calabria, Rende, Italy

    Sergio Greco

  3. ICAR-CNR, Rende, Italy

    Elio Masciari

  4. University of Calabria, Rende, Italy

    Domenico Saccà

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Angelini, M., Catarci, T., Mecella, M., Santucci, G. (2018). The Visual Side of the Data. In: Flesca, S., Greco, S., Masciari, E., Saccà, D. (eds) A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years. Studies in Big Data, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-319-61893-7_1

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