Visualization is the process of mapping data into visual dimensions to create a visual representation to amplify cognition. Visual representations are essential aids to human cognitive tasks and are valued to the extent that they provide stable and external reference points upon which dynamic activities and thought processes may be calibrated and upon which models and theories can be tested and confirmed. The active use and manipulation of visual representations makes many complex and intensive cognitive tasks feasible. A visual representation is able to convey relationships among many elements in parallel and provides an individual with directly observable memory. A successful visualization allows the user to gain insight into the data, in other words to communicate different aspect of the data in an effective way. Even with today’s visualization systems that give the user a considerable control over the visualization process, it can be difficult to produce an effective visualization. To obtain useful results, a user had to know which questions to pose. Problems had to be framed in very precise terms. A strategy to improve this situation is to guide the user in the selection of the parameters involved in the visualization. Our research goal is the design of a visualization system that assist the user to do the work, by considering the semantic of the data together with the semantic of the stages through all the visualization process.Eje: Computación Gráfica, Visualización e ImágenesRed de Universidades con Carreras en Informática (RedUNCI

Castro, Silvia Mabel

Larrea, Martín Leonardo

Martig, Sergio R.

English

El Servicio de Difusión de la Creación Intelectual

Semantic Based Visualization 
Martín Larrea1 
Sergio Martig1 
Silvia Castro1 
1Departamento de Ciencias e Ingeniería de la Computación 
Laboratorio de Investigación en Visualización y Computación Gráfica 
Universidad Nacional del Sur 
{mll, srm, smc}@cs.uns.edu.ar 
Tel. 54-291-4595135 
Fax. 54-291-4595136 
Bahía Blanca, CP 8000, Argentina 
 
ABSTRACT 
Visualization is the process of mapping data into visual dimensions to create a visual representation 
to amplify cognition. Visual representations are essential aids to human cognitive tasks and are 
valued to the extent that they provide stable and external reference points upon which dynamic 
activities and thought processes may be calibrated and upon which models and theories can be 
tested and confirmed. The active use and manipulation of visual representations makes many 
complex and intensive cognitive tasks feasible. A visual representation is able to convey 
relationships among many elements in parallel and provides an individual with directly observable 
memory. A successful visualization allows the user to gain insight into the data, in other words to 
communicate different aspect of the data in an effective way. Even with today’s visualization 
systems that give the user a considerable control over the visualization process, it can be difficult to 
produce an effective visualization. To obtain useful results, a user had to know which questions to 
pose. Problems had to be framed in very precise terms. A strategy to improve this situation is to 
guide the user in the selection of the parameters involved in the visualization. Our research goal is 
the design of a visualization system that assist the user to do the work, by considering the semantic 
of the data together with the semantic of the stages through all the visualization process. 
Keywords: Semantic, Visualization pipeline. 
 
1. INTRODUCTON 
The visualization challenge is to find a visual metaphor that the user can understand and perceive 
effectively [1] [2] [3], and to provide interaction methods [4] that make it  possible for the user to 
work with and probe the data as effectively and effortlessly as possible. Computer technology 
allows the exploration of big information resources. Huge amount of data are becoming available 
on networked information systems, ranging from unstructured and multimedia documents to 
structured data stored in databases. On one side, this is extremely useful and exciting. On the other 
side, the ever growing amount of available information generates cognitive overload and even 
anxiety, especially in novice or occasional users. While computational power has increased 
exponentially, the ability to interact with useful information has only increased incrementally. In 
recent decades, the exponential increase in computing power has allowed many more questions to 
be posed and more complex problems to be addressed. Information is now massive, disparate, and 
disorganized. The dimensionality of data has also increased, requiring greater effort to identify and 
comprehend relationships relevant to a particular analytic task. 
Nowadays, a wide diversity of users access, extract, and display information that is distributed on 
various sources, which differ in type, form and content. In many cases the users have an active 
control over the visualization process but even then it is difficult to achieve an effective 
visualization. For example, since the goal of visualization is to provide a representation which helps 
them to interpret their data or to communicate meaning, it is important that the mapping from 
physical to perceptual dimensions be under control. A strategy to improve this situation is to guide 
the user in the selection of the different parameters involved in the visualization. The Visualization 
field has matured substantially during the last decade; new techniques have appeared for different 
data types in many domains. With the use of visualization becoming more generalized, a formal 
understanding of the visualization process is needed.  
 
2. PREVIOUS WORK 
 
2.1 RULE-BASED ARCHICTURE EXAMPLE 
PRAVDA (Perceptual Rule-Based Architecture for Visualizing Data Accurately) [5] is a rule based 
architecture for assisting the user in making choices of visualization color parameters. This 
architecture provides sets of appropriates choices for visualization based on a set of underlying rules 
[6] [7] which are used to constrain operations i.e., selecting a colormap. Rules incorporate 
information about data, which is call metadata, such as minimum, maximum, or spatial frequency, 
and also information supplied by the user. 
This architecture also provides for linkages between rules that control different visualization 
operations, with a choice of parameters for one operation constraining choices that are available for 
others. For example, if the user selects a colormap, that information is fed back to the operation for 
selecting contour lines, where rules constrain the parameters of the contour lines depending on 
which colormap has been selected. Hence, if the contour lines are superimposed over a dark region, 
as defined by the colormap, legibility rules would constrain the set of color choices to those offering 
sufficient luminance contrasts to be detectable. This network of linked operations help guide the 
user through the complex design space of visualization operations. The key element in this rule 
based architecture is the use of metadata; system provided metadata, as data type, data range, 
metadata computed by algorithm, as spatial frequency, and metadata provided by the user. These 
metadata would, for example, represent the dynamic range of the data or the geometric relationships 
between objects in the scene. 
 
2.2 SEMANTIC VISUALIZATON OF BIOCHEMICAL DATABASES 
Extracting and visualizing information from biochemical databases is one of the most important 
challenges in biochemical research. The huge quantity and high complexity of the data available 
force the biologist to use sophisticated tools for extracting and interpreting accurately the 
information extracted from the database. These tools must define a graphical semantics associated 
to the data semantics in accordance with biologist usages. The worked done in [14] define a 
customizable representation model which allows the biologist to change the graphical semantics 
associated to the data semantics. The representation models are base on an XML implementation; 
such models are based on an XML Schema definition that prescribes the correctness of the model 
and provides validation features. This project include the development of tools for discovering 
recurring topological patters among biochemical networks; these tools are developed using 
constraint logic programming. 
 
3. VISUALIZATON PROCESS 
The different visualization models presented in the last years cover partially the aspects of the 
exploration process; Upson [8] and Card [9] models give an overview of the visualization process 
but do not offer enough details for the user exploration. Chi model [10] does not describe properly 
the interactions and Chuah and Roth model [11], presents a detailed definition of the interactions, 
but does not seem to be enough to cover all the possible applications. In order to overcame these 
problem we have developed a model that represents all the visualization process stages and the 
interactions between them and the user. The “Unified Visualization Model” [12] was developed to 
create an unified conceptual framework, independent from the data domain. This model takes under 
consideration the characteristics of all visualization areas. The unified model focuses on the 
visualization processes as well as in the data stages. (See. Figure 1) In this model, the user’s 
interactions play a central point, because is the user who interacts with the visualization and, based 
on his/her interpretations of the representation, modifies the image to steer the calculation, remap 
the data representation in order to better understand its structure, or create a visualization which 
highlights a particular feature. 
This model is represented by stages along a flow, the flow represent the transformations of data. 
Each stage is a data stage and the edges are the transformations to move from one stage to the next. 
The unified model considers five stages and four transformations. The transformations and the 
stages along this flow are a reflexion of the user interaction on the visualization process. We present 
now a brief description of the stages and transformation in the Unified Visualization Model. 
 
 
Figure 1. The visualization pipeline 
The “Unified Visualization Model” 
Stages Transformations 
Stage “Raw data” 
Data from the application domain. 
Transformation “Raw data to Abstract Data” 
This transformation allows the user to select the data 
he/she wants to visualize. After the selection, the data 
moves from the data domain representation to an inner 
and manageable structure. 
Stage “Abstract Data” 
Data to be potentially visualize by the user. Besides this 
data the user also has the metadata created in the previous 
transformation. 
Transformation “Abstract data to Data to be 
Visualize” 
From the “Abstract data” stage the user will select all the 
data that will be visualized. 
Stage “Data to be Visualize” 
Data that will be visualized. It can be a subset of the 
“Abstract data” 
 
Transformation “Visual Mapping” 
This transformation allows the user to specify how he/she 
wants to visualize all the data in the previous stage. All the 
necessary structures to support the spatial substrate, the 
visual elements and their attributes are created from this 
transformation. 
Stage “Visual Mapped Data” 
Data to be visualized along with all the necessary 
information for its visual representation. 
Transformation “Visualization Transformation” 
This transformation allows the creation on screen of all 
the data in the “Visual Mapped Data”. This will usually 
include the application of some visualization technique 
that supports all the restrictions imposed in the “Visual 
Mapping” transformation. 
Stage “Visualize data” 
This is the result from the visualization process. This is 
the starting point for the user to begin his/her visual 
exploration and navigation process. 
 
 
 
4. OUR GOAL 
The user is an active participant in the visualization process, and the goal of visualization is to 
present data in a way which helps him/her identify trends, features and patterns, generate 
hypotheses, and assign meaning to visual information on screen. Our goal is to develop a 
visualization model that considers the semantic of the data and of the different stages in the 
visualization process. This model will transform data into information; according to Keller and 
Tergan [13], “information is data that has been given meaning through interpretation by way of 
relational connection and pragmatic context”. The information is the same only for those people 
who attribute to it the same meaning. This ‘meaning’ can be useful, but does not have to be. 
Information may be distinguished according to different categories concerning, for instance, its 
features, origin and relations. By making these considerations, the visualization process will be able 
to determinate the characteristics of an effective visualization guiding the user through the different 
stages. The metadata will define a higher level characterization of the data which provides a higher 
level interface to the user, and a higher level input to visualization rules. All the data from the 
different application domains will be categorized according to [9]. 
At present, we are surveying the visualization techniques and the different data models and 
interactions involved. For each technique we will study its interactions under representative 
application domains. All these techniques will be analyzed in the context of the “Unified 
Visualization Model” [12]. Taking all this into account we will begin to define the semantic of the 
stages involved in the visualization process. Our goal is to define an unified semantic for the data 
model and the process involved. We have concluded that the first stage of the “Unified 
Visualization Model”, the raw data, will include an XML representation of the input data and with 
this the associated semantic; both RDF and OWL are being consider for the XML representation. 
All the final and intermediate results will be publish. 
This work is in progress at the “Laboratorio de Investigación y Desarrollo en Visualización y 
Computación Gráfica”, Computers Sciences and Engineering Department, Universidad Nacional 
del Sur. This work is close related with the next research projects: 
 
- “Modelo Unificado de Visualización. Operadores y Operandos” (24/N015). Director: Dra. Silvia 
Castro. Co-Director: Sergio Martig.   
 
- “Desarrollo de Herramientas Inteligentes para la Web Semántica” (PICT año 2003 Nro 15043).  
 
- “Sistemas Inteligentes para apoyo a los Procesos Productivos”, Subproyecto Servicios de WEB e 
Inteligencia en la WEB, (PAV año 2003 Nro. 00076). 
 
In conclusion we consider that a visualization process model with its proper interactions is not 
enough to assure an effective visualization. To achieve this, a meta-data model for the visualization 
process, visualization stages, data and interactions also need to be developed. 
 
4. ACKNOWLEDGMENTS 
This work was partially supported by the PGI 24/N015, Science and Technology Department of the 
Universidad Nacional del Sur (SECyT, UNS) and the PICT 2003 Nº 15043, Agencia Nacional de 
Promoción Científica y Tecnológica. 
 
5. REFERENCES 
[1]   Bertin, J. “Semiology of Graphics”, 1967, reprinted by University of Wisconsin Press, 
Madison, WI, 1983. 
[2]   Tufte, E. “The Visual Display of Quantitative Information”. Connecticut: Graphics Press, 
1983. 
[3]  Cleveland, W.S. “The Elements of Graphing Data”, Monterey, California: Wadsworth, Inc., 
1991. 
[4] Baecker, R. M. and Buxton, W. A. S., “Readings in Human-Computer Interaction”, San 
Mateo CA., Morgan Kaufmann Publishers, 1995. 
[5] Bergman, L. D., Rogowitz, E. B., Treinish, A. L., “A Rule-based Tool for Assisting 
Colormap Selection”. IBM Thomas J. Watson Research Center, Yorktown Heights, NY. 
[6] Rogowitz, E. B., Treinish, A. L., “Data Structures and Perceptual Structures”.  SPIE – The 
International Society for Optical Engineering. February 1993. Volume 1913. 
[7] Rogowitz, E. B., Treinish, A. L., “Using Perceptual Rules in Interactive Visualization”. 
SPIE – The International Society for Optical Engineering. February 1993. Volume 2179. 
[8] Upson, C., Faulhaber, T., Kamins, D., Laidlaw, D., Schlegel, D., Vroom, J., Gurwitz, R., 
van Dam, A., “The application visualization system: A computational environment for 
scientific visualization”. CG&A, IEEE, 9(4):30–42, 1989. 
[9] Card, S., Mackinlay, J., Shneiderman, B., “Readings in Information Visualization – Using 
Vision to Think”, Morgan Kaufmann, 1999. 
[10] Chi,E.; “A Taxonomy of Visualizations Techniques using the data state reference model”, 
InfoVis 2000, pp. 69-75. IEEE Publishers, 2000. 
[11] Chuah, M.C., Roth, S.F., “On the Semantics of Interactive Visualizations”, Proceedings of 
the INFOVis 96, pp. 29-36. IEEE Publishers, 1996. 
[12]  Martig, S., Castro,S., Fillottrani, P. & Estévez, E., “Un Modelo Unificado de Visualización”. 
Proceedings, pp. 881-892, 9º Congreso Argentino de Ciencias de la Computación. Oct.2003. 
Argentina. 
[13] Keller T, Tergan S-O. Visualizing knowledge and information: an introduction. In: Tergan 
S-O and Keller T (Eds). Knowledge and Information Visualization: Searching for Synergies. 
Lecture Notes in Computer Science LNCS 3426. Springer: Berlin, Heidelberg, New York, 
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[14]  Semantics of a networked world: Semantic Visualization of Biochemical Databases  (Paris, 
17-19 June 2004, revised selected papers)  ICSNW 2004 : international IFIP conference 
No1, Paris , FRANCE (17/06/2004)  20041973, vol. 3226, pp. 199-214. ISBN 3-540-23609-
0. 


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