This paper proposes a conceptual model called compound brushing for modeling the brushing techniques used in dynamic data visualization. In this approach, brushing techniques are modeled as higraphs with five types of basic entities: data, selection, device, renderer, and transformation. Using this model, a flexible visual programming tool is designed not only to configure and control various common types of brushing techniques currently used in dynamic data visualization but also to investigate new brushing techniques.
BeckerRAClevelandWSWilksAR. Dynamic graphics for data analysis. In: ClevelandWSMcGillR (Eds.). Dynamic Graphics for Statistics. Wadsworth & Brooks/Cole: Pacific Grove, CA, 1988.
3.
VellemanPF. The Datadesk Handbook. Odesta Corporation: Ithaca, NY, 1988.
4.
WardM.Xmdvtool: integrating multiple methods for visualizing multivariate data. Proceedings of IEEE Visualization1994. (Washington, DC, USA), IEEE Computer Society Press: Chicago, 1994; 326–333.
5.
MartinAWardM.High dimensional brushing for interactive exploration of multivariate data. Proceedings of IEEE Visualization1995. (Atlanta, GA, USA), IEEE Computer Society Press: Silver Spring, MD, 1995; 271–278.
6.
WillsGJ. Selection: 524,288 ways to say 'this is interesting'. Proceedings of IEEE Symposium on Information Visualization 1996. (San Francisco, CA, USA), IEEE Computer Society: Silver Spring, MD, 1996; 54–60.
7.
HauserHLedermannFDoleischH.Angular brushing of extended parallel coordinates. Proceedings of IEEE Symposium on Information Visualization 2002. (Boston, MA, USA), IEEE Computer Society Press: Los Alamitos, CA, 2002; 127–130.
8.
FuaYWardMRundensteinerE.Structure-based brushes: A mechanism for navigating hierarchically organized data and information spaces. IEEE Transactions on Visualization and Computer Graphics2000; 6: 150–159.
9.
KeimDA. Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics2002; 8: 1–8.
10.
DerthickMKolojejchickJRothSF. An interactive visual query environment for exploring data. Proceedings of ACM Symposium on User Interface Software and Technology 1997. (Banff, Alberta, Canada), ACM Press: New York, 1997; 189–198.
11.
AhlbergCShneidermanB.Visual information seeking: tight coupling of dynamic query filters with starfield displays. Proceedings of ACM Conference on Human factors in Computing Systems CHI1994. (Boston, MA, USA), ACM Press: New York, 1994; 313–317.
12.
LivnyMRamakrishnanRBeyerKChenGDonjerkovicDLawandeSMyllymakiJWengerK.DEVise: integrated querying and visual exploration of large datasets. Proceedings of ACM SIGMOD 1997. (Tucson, AZ, USA), ACM Press: New York, 1997; 301–312.
13.
NorthCShneidermanB.Snap-together visualization: a user interface for coordinating visualizations via relational schemata. Proceedings of the Working Conference on Advanced Visual Interfaces 2000. (Palermo, Italy), World Scientific Publishing Company, Inc. Singapore, 2000; 128–135.
14.
NorthCConklinNIndukuriKSainiV.Visualization schemas and a web-based architecture for custom multiple-view visualization of multiple-table databases. Information Visualization2002; 1: 211–228.
15.
WongPCBergeronRD. Brushing techniques for exploring volume datasets. Proceedings of IEEE Visualization 1997. (Phoenix, AZ, USA), IEEE Computer Society: Los Alamitos, 1997; 429–432.
16.
HarelD.On visual formalisms. Communications of the ACM1988; 31: 514–530.
17.
ChenH.Towards design patterns for dynamic analytical data visualization. Proceedings of SPIE Conference on Visualization and Data Analysis 2004. (San Jose, CA, USA), Vol. 5295, 2004, to appear.
18.
CardSKMackinlayJDShneidermanB.Readings in Information Visualization: Using Vision to Think. Morgan Kaufman Publishers: San Francisco, CA, 1999.
19.
ChiEH. A taxonomy of visualization techniques using the data state reference model. Proceedings of IEEE Symposium on Information Visualization 2000. (Salt Lake City, UT, USA), IEEE Computer Society Press: Silver Spring, MD, 2000; 69–75.
20.
MooreDSMcCabeGP. Introduction to the Practice of Statistics. W. H. Freeman and Company: New York, NY, 1989.
21.
JinWRileyRMWolfingerRDWhiteKPPassador-GurgelGGibsonG.The contributions of sex, genotype and age to transcriptional variance in drosophila melanogaster. Nature Genetics2001; 29: 389–395.
22.
WolfingerRDGibsonGWolfingerEDBennettLHamadehHBushelPRAfshariCAPaulesRS. Assessing gene significance from cDNA microarray expression data via mixed models. Journal of Computational Biology2001; 8: 625–637.
23.
CraigPHaslettJUnwinAWillsG.Moving statistics: an extension of brushing for spatial data. Computer Science and Statistics: Proceedings of the 21st Symposium on the Interface. American Statistical Association: Alexandria, VA, 1989; 170–174.
24.
HaslettJBradleyRCraigPUnwinAWillsG.Dynamic graphics for exploring spatial data with application to locating global and local anomalies. The American Statistician1991; 45: 234–242.
25.
ChenH.Compound brushing. Proceedings of IEEE Symposium on Information Visualization 2003. (Seattle, WA, USA), IEEE Computer Society Press: Los Alamitos, CA., 2003; 181–188.
