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.
AmarRAStaskoJT. Knowledge precepts for design and evaluation of information visualizations. IEEE T Vis Comput Gr2005; 11(4): 432–442.
6.
LeeBPlaisantCParrCS. 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.
7.
ShneidermanBArisA. Network visualization by semantic substrates. IEEE T Vis Comput Gr2006; 12(5): 733–740.
8.
LiuZStaskoJ. Mental models, visual reasoning and interaction in information visualization: a top-down perspective. IEEE T Vis Comput Gr2010; 16(6): 999–1008.
AdarE. 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.
HansenDShneidermanBSmithMA. Analyzing social media networks with NodeXL: insights from a connected world. San Francisco, CA: Morgan Kaufmann, 2010.
14.
HenryNFeketeJMcGuffinMJ. NodeTrix: a hybrid visualization of social networks. IEEE T Vis Comput Gr2007; 13(6): 1302–1309.
15.
PererAShneidermanB. Balancing systematic and flexible exploration of social networks. IEEE T Vis Comput Gr2006; 12(5): 693–700.
16.
LiuZNavatheSStaskoJ. 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.
LivnyMRamakrishnanRBeyerK. DEVise: integrated querying and visual exploration of large datasets. SIGMOD Rec1997; 26(2): 301–312.
18.
RaoRCardSK. 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.
SpenkeMBeilkenCBerlageT. 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.
SpenkeMBeilkenC. 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.
StolteCTangDHanrahanP. Polaris: a system for query, analysis, and visualization of multidimensional relational databases. IEEE T Vis Comput Gr2002; 8(1): 52–65.
HeerJCardSKLandayJA. 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.
Gephi: an open source graph visualization and manipulation software, http://gephi.org (2010).
27.
AuberD. Tulip: a huge graph visualization framework. In: JüngerMMutzelP (eds) Graph drawing software (mathematics and visualization). Berlin: Springer-Verlag, 2003, pp. 105–123.
28.
FreireMPlaisantCShneidermanB. 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.
WattenbergM. 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.
GuéhisSRigauxPWallerE. 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.
BohannonPKorthHFNarayanPPS. 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.
WilkinsonL. The grammar of graphics. NY: Springer-Verlag, 2005.
33.
WeaverC. 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.
HeerJPererA. 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.
WeaverC. Cross-filtered views for multidimensional visual analysis. IEEE T Vis Comput Gr2010; 16(2): 192–204.
38.
NorthCConklinNIndukuriK. Visualization schemas and a web-based architecture for custom multiple-view visualization of multiple-table databases. Inform Visual2002; 1(3–4): 211–228.
39.
DijkstraEW. A note on two problems in connexion with graphs. Numer Math1959; 1: 269–271.
40.
LatapyMMagnienCVecchioND. Basic notions for the analysis of large two-mode networks. Soc Networks2008; 30(1): 31–48.
41.
CypherAHalbertDC. Watch what I do: programming by demonstration. Cambridge, MA: The MIT Press, 1993.
KamadaTKawaiS. An algorithm for drawing general undirected graphs. Inform Process Lett1989; 31(1): 7–15.
44.
MeyerB. Self-organizing graphs: a neural network perspective of graph layout. In: WhitesidesSue (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.
ClevelandWSMcGillR. Graphical perception and graphical methods for analyzing scientific data. Science1985; 229(4716): 828–833.
46.
MackinlayJ. Automating the design of graphical presentations of relational information. ACM T Graphic1986; 5(2): 110–141.
47.
MackinlayJDHanrahanPStolteC. Show me: automatic presentation for visual analysis. IEEE T Vis Comput Gr2007; 13(6): 1137–1144.
48.
WareC. Visual thinking for design. Burlington, MA: Morgan Kaufmann, 2008.
49.
WareC. Information visualization: perception for design. San Francisco, CA: Morgan Kaufmann, 2004.
50.
SmithMAShneidermanBMilic-FraylingN. 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.
MeijerEBiermanG. A co-relational model of data for large shared data banks. Queue2011; 9(3): 30–48.
56.
Van HamFPererA. “Search, Show Context, Expand on Demand”: supporting large graph exploration with degree-of-interest. IEEE T Vis Comput Gr2009; 15(6): 953–960.
57.
NormanDA. The design of everyday things. NY: Basic Books, 2002.
58.
GrammelLToryMStoreyM. How information visualization novices construct visualizations. IEEE T Vis Comput Gr2010; 16(6): 943–952.
59.
KobsaA. 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.
