Ploceus: Modeling,visualizing,and analyzing tabular data as networks

Abstract

Tabular data are pervasive. Although tables often describe multivariate data without explicit definitions of a network, it may be advantageous to explore the data by modeling it as a graph or network for analysis. Even when a given table design specifies a network structure, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics. We present a system called Ploceus that offers a general approach for performing multidimensional and multilevel network–based visual analysis on multivariate tabular data. Powered by an underlying relational algebraic framework, Ploceus supports flexible construction and transformation of networks through a direct manipulation interface and integrates dynamic network manipulation with visual exploration through immediate feedback mechanisms. We report our findings on the learnability and usability of Ploceus and propose a model of user actions in visualization construction using Ploceus.

Keywords

Data transformations graph and network visualization multivariate data interaction design exploratory data analysis

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References

Chen

. The entity-relationship model—toward a unified view of data. ACM T Database Syst 1976; 1(1): 9–36.

Elmasri

Navathe

. Fundamentals of database systems. 6th ed. Boston, MA: Addison-Wesley, 2011.

Chen

Yan

Zhu

. Graph OLAP: a multi-dimensional framework for graph data analysis. Knowl Inf Syst 2009; 21: 41–63.

Bagui

Earp

. Database design using entity-relationship diagrams. 1st ed. Boston, MA: Auerbach Publications, 2003.

Amar

Stasko

. Knowledge precepts for design and evaluation of information visualizations. IEEE T Vis Comput Gr 2005; 11(4): 432–442.

Lee

Plaisant

Parr

. Task taxonomy for graph visualization. In: Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization, Venice, Italy, 23 May 2006, pp. 1–5. NY: ACM Press.

Shneiderman

Aris

. Network visualization by semantic substrates. IEEE T Vis Comput Gr 2006; 12(5): 733–740.

Liu

Stasko

. Mental models, visual reasoning and interaction in information visualization: a top-down perspective. IEEE T Vis Comput Gr 2010; 16(6): 999–1008.

NetMiner—social network analysis software, http://www.netminer.com (2010).

10.

Pajek—program for large network analysis, http://pajek.imfm.si/doku.php (2010).

11.

UCINET, http://www.analytictech.com/ucinet/ (2010).

12.

Adar

. GUESS: a language and interface for graph exploration. In: Proceedings of the SIGCHI conference on Human Factors in computing systems, Quebec, Canada, 22-27 April 2006.

13.

Hansen

Shneiderman

Smith

. Analyzing social media networks with NodeXL: insights from a connected world. San Francisco, CA: Morgan Kaufmann, 2010.

14.

Henry

Fekete

McGuffin

. NodeTrix: a hybrid visualization of social networks. IEEE T Vis Comput Gr 2007; 13(6): 1302–1309.

15.

Perer

Shneiderman

. Balancing systematic and flexible exploration of social networks. IEEE T Vis Comput Gr 2006; 12(5): 693–700.

16.

Liu

Navathe

Stasko

. Network-based visual analysis of tabular data. In: IEEE conference on visual analytics science and technology ’11, Providence, RI, 23-28 October, 2011, pp. 41–50. Piscataway, NJ: IEEE Press.

17.

Livny

Ramakrishnan

Beyer

. DEVise: integrated querying and visual exploration of large datasets. SIGMOD Rec 1997; 26(2): 301–312.

18.

Rao

Card

. The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In: Proceedings of the SIGCHI conference on human factors in computing systems: celebrating interdependence, Boston, Massachusetts, USA, 24-28 April 1994, pp. 318–322. Boston, MA: ACM.

19.

Spenke

Beilken

Berlage

. FOCUS: the interactive table for product comparison and selection. In: Proceedings of the 9th annual ACM symposium on user interface software and technology, Seattle, WA, USA, 6-8 November 1996, pp. 41–50. New York, NY: ACM Press.

20.

Spenke

Beilken

. InfoZoom: analysing formula one racing results with an interactive data mining and visualisation tool. In: Proceedings of 2nd international conference on data mining, Cambridge University, Cambridge, UK, 5-7 July 2000, pp. 455–464. Southampton, UK: WIT Press.

21.

Stolte

Tang

Hanrahan

. Polaris: a system for query, analysis, and visualization of multidimensional relational databases. IEEE T Vis Comput Gr 2002; 8(1): 52–65.

22.

Tableau Software, http://www.tableausoftware.com/ (2010).

23.

Stasko

Görg

Liu

. Jigsaw: supporting investigative analysis through interactive visualization. Inform Visual 2008; 7(2): 118–132.

24.

Heer

Card

Landay

. Prefuse: a toolkit for interactive information visualization. In: Proceedings of the SIGCHI conference on human factors in computing systems, Portland, Oregon, 2-7 Apr 2005, pp. 421–430. NY: ACM Press.

25.

O’Madadhain

Fisher

White

. JUNG: Java universal network/graph framework. Technical report UCI-ICS 03-17, 2003.

26.

Gephi: an open source graph visualization and manipulation software, http://gephi.org (2010).

27.

Auber

. Tulip: a huge graph visualization framework. In: Jünger

Mutzel

(eds) Graph drawing software (mathematics and visualization). Berlin: Springer-Verlag, 2003, pp. 105–123.

28.

Freire

Plaisant

Shneiderman

. ManyNets: an interface for multiple network analysis and visualization. In: Proceedings of the 28th international conference on human factors in computing systems, Atlanta, GA, 10-15 April 2010, pp. 213–222. NY: ACM Press.

29.

Wattenberg

. Visual exploration of multivariate graphs. In: Proceedings of the SIGCHI conference on human factors in computing systems, Montreal, Quebec, 22-27 April 2006, pp. 811–819. New York: ACM.

30.

Guéhis

Rigaux

Waller

. Data-driven publication of relational databases. In: 10th international database engineering and applications symposium, Delhi, India, 11-14 December 2006, pp. 267–272. Piscataway, NJ: IEEE Press.

31.

Bohannon

Korth

Narayan

PPS

. The table and the tree: on-line access to relational data through virtual XML documents. In: Proceedings of WebDB, Santa Barbara, California, USA, 24-25 May 2001, pp. 55–60. NY: ACM.

32.

Wilkinson

. The grammar of graphics. NY: Springer-Verlag, 2005.

33.

Weaver

. Multidimensional data dissection using attribute relationship graphs. In: IEEE symposium on visual analytics science and technology (VAST), Salt Lake City, Utah, 24-29 October 2010, pp. 75–82. Piscataway, NJ: IEEE Press.

34.

TouchGraph Navigator: graph visualization and social network analysis software, http://touchgraph.com/navigator (2011).

35.

Centrifuge systems, http://centrifugesystems.com/ (2011).

36.

Heer

Perer

. Orion: a system for modeling, transformation and visualization of multidimensional heterogeneous networks. In: IEEE conference on visual analytics science and technology ’11, Providence, RI, 23-28 October, 2011. Piscataway, NJ: IEEE Press.

37.

Weaver

. Cross-filtered views for multidimensional visual analysis. IEEE T Vis Comput Gr 2010; 16(2): 192–204.

38.

North

Conklin

Indukuri

. Visualization schemas and a web-based architecture for custom multiple-view visualization of multiple-table databases. Inform Visual 2002; 1(3–4): 211–228.

39.

Dijkstra

. A note on two problems in connexion with graphs. Numer Math 1959; 1: 269–271.

40.

Latapy

Magnien

Vecchio

. Basic notions for the analysis of large two-mode networks. Soc Networks 2008; 30(1): 31–48.

41.

Cypher

Halbert

. Watch what I do: programming by demonstration. Cambridge, MA: The MIT Press, 1993.

42.

Fruchterman

Reingold

. Graph drawing by force-directed placement. Software Pract Exper 1991; 21(11): 1129–1164.

43.

Kamada

Kawai

. An algorithm for drawing general undirected graphs. Inform Process Lett 1989; 31(1): 7–15.

44.

Meyer

. Self-organizing graphs: a neural network perspective of graph layout. In: Whitesides

Sue

(ed.) Proceedings of the 6th International Symposium on Graph Drawing (GD ’98), Montreal, Canada, 13-15 August 1998, pp. 246-262. London, UK: Springer-Verlag.

45.

Cleveland

McGill

. Graphical perception and graphical methods for analyzing scientific data. Science 1985; 229(4716): 828–833.

46.

Mackinlay

. Automating the design of graphical presentations of relational information. ACM T Graphic 1986; 5(2): 110–141.

47.

Mackinlay

Hanrahan

Stolte

. Show me: automatic presentation for visual analysis. IEEE T Vis Comput Gr 2007; 13(6): 1137–1144.

48.

Ware

. Visual thinking for design. Burlington, MA: Morgan Kaufmann, 2008.

49.

Ware

. Information visualization: perception for design. San Francisco, CA: Morgan Kaufmann, 2004.

50.

Smith

Shneiderman

Milic-Frayling

. Analyzing (social media) networks with NodeXL. In: Proceedings of the fourth international conference on communities and technologies, University Park, PA, USA, 25-27 June 2009, pp. 255–264. University Park, PA: ACM.

51.

GraphML file format, http://graphml.graphdrawing.org/ (2010).

52.

Codd

. A relational model of data for large shared data banks. Commun ACM 1970; 13(6): 377–387.

53.

NetBeans rich-client platform development, http://netbeans.org/features/platform/ (2011).

54.

H2 database engine, http://www.h2database.com/html/main.html (2011).

55.

Meijer

Bierman

. A co-relational model of data for large shared data banks. Queue 2011; 9(3): 30–48.

56.

Van Ham

Perer

. “Search, Show Context, Expand on Demand”: supporting large graph exploration with degree-of-interest. IEEE T Vis Comput Gr 2009; 15(6): 953–960.

57.

Norman

. The design of everyday things. NY: Basic Books, 2002.

58.

Grammel

Tory

Storey

. How information visualization novices construct visualizations. IEEE T Vis Comput Gr 2010; 16(6): 943–952.

59.

Kobsa

. An empirical comparison of three commercial information visualization systems. In: IEEE symposium on information visualization, San Diego, California, 22-23 October 2001, pp. 123–130. Piscataway, NJ: IEEE Press.