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SECTION II
TechnoIogy
and Systems
CHAPTER 4
Information Visualization
Bin Zhu
Boston University
Hsinchun Chen
The University of Arizona
Introduction
Advanced technology has resulted in the generation of about one million terabytes of information every year. Ninety-nine percent of this is
available in digital format (Keim, 2001). More information will be generated in the next three years than was created during all of previous
human history (Keim, 2001). Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult. Various search engines have been developed to
make it easier to locate information of interest, but these work well only
for a person who has a specific goal and who understands what and how
information is stored. This usually is not the case.
Visualization was commonly thought of iri terms of representing
human mental processes (MacEachren, 1991; Miller, 1984). The concept
is now associated with the amplification of these mental processes
(Card, Mackinlay, & Shneiderman, 1999). Human eyes can process
visual cues rapidly, whereas advanced information analysis techniques
transform the computer into a powerful means of managing digitized
information. Visualization offers a link between these two potent systems, the human eye and the computer (Gershon, Eick, & Card, 19981,
helping to identify patterns and to extract insights from large amounts
of information. The identification of patterns is important because it
may lead to a scientific discovery, an interpretation of clues to solve a
crime, the prediction of catastrophic weather, a successful financial
investment, or a better understanding of human behavior in a computermediated environment. Visualization technology shows considerable
promise for increasing the value of large-scale collections of information,
as evidenced by several commercial applications of TreeMap (e.g.,
http://www.smartmoney.com) and Hyperbolic tree (e.g., http://www.inxightxom) to visualize large-scale hierarchical structures.
Although the proliferation of visualization technologies dates from
the 1990s when sophisticated hardware and software made increasingly
139
140 Annual Review of Information Science and Technology
faster generation of graphical objects possible, the role of visual aids in
facilitating the construction of mental images has a long history.
Visualization has been used to communicate ideas, to monitor trends
implicit in data, and to explore large volumes of data for hypothesis generation. Imagine traveling to a strange place without a map, having to
memorize physical and chemical properties of an element without
Mendeleyev’s periodic table, trying to understand the stock market
without statistical diagrams, or browsing a collection of documents without interactive visual aids. A collection of information can lose its value
simply because of the effort required for exhaustive exploration. Such
frustrations can be overcome by visualization.
Visualization can be classified as scientific visualization, software
visualization, or information visualization. Although the data differ, the
underlying techniques have much in common. They use the same elements (visual cues) and follow the same rules of combining visual cues
to deliver patterns. They all involve understanding human perception
(Encarnacao, Foley, Bryson, & Feiner, 1994) and require domain knowledge (Tufte, 1990).
Because most decisions are based on unstructured information, such
as text documents, Web pages, or e-mail messages, this chapter focuses
on the visualization of unstructured textual documents. The chapter
reviews information visualization techniques developed over the last
decade and examines how they have been applied in different domains.
The first section provides the background by describing visualization
history and giving overviews of scientific, software, and information
visualization as well as the perceptual aspects of visualization. The next
section assesses important visualization techniques that convert
abstract information into visual objects and facilitate navigation
through displays on a computer screen. It also explores information
analysis algorithms that can be applied to identify or extract salient
visualizable structures from collections of information. Information
visualization systems that integrate different types of technologies to
address problems in different domains are then surveyed; and we move
on to a survey and critique of visualization system evaluation studies.
The chapter concludes with a summary and identification of future
research directions.
Overview of Visualization
History and Background
Although (computer-based) visualization is a relatively new research
area, visualization has a long history. For instance, the first known map
was created in the 12th century (Tegarden, 1999), and multidimensional
representations appeared in the 19th century (Tufte, 1983). Bertin (1967)
identified basic elements of diagrams in 1967, and Tufte (1983) published
his theory regarding maximizing the density of useful information in
Information Visualization 141
1983. Both Bertin’s and Tufte’s theories have had substantial impact on
subsequent information visualization. Most early visualization research
focused on statistical graphics (Card et al., 1999) until the data explosion of the 1980s when supercomputers were able to run complex simulation models and advanced scientific sensors also generated huge
quantities of data (Nielson, 1991). Researchers from earth science,
physics, chemistry, biology, and computer science turn to visualization
for help in analyzing copious data and identifying patterns. The
National Science Foundation (NSF) launched its “scientific visualization” initiative in 1985 (McCormick, Defanti, & Brown, 1987) and the
Institute of Electrical and Electronic Engineers (IEEE) held its first
visualization conference in 1990.
At the same time, visualization technologies were being applied in
many nonscientific contexts, including business, digital libraries, human
behavior, and the Internet. As the application domains expanded and
computer hardware and software became more powerful and affordable,
visualization techniques continued to improve. Since 1990, a vast
amount of nonscientific data has been generated as a consequence of
easy information creation and the emergence of the Internet. The term
“information visualization” was first used in Robertson, Card, and
Mackinlay (1989) to denote the presentation of abstract information
through a visual interface. Early information visualization systems
emphasized interactivity and animation (Robertson, Card, & Mackinlay,
19931, interfaces to support dynamic queries (Shneiderman, 19941, and
various layout algorithms on a computer screen (Lamping, Rao, &
F’irolli, 1995). Later visualization systems presenting the subject hierarchy of the Internet (H. Chen, Houston, Sewell, & Schatz, 1998), summarizing the contents of a document (Hearst, 19951, describing online
behaviors (Donath, 2002; Zhu & Chen, 2001), displaying Web site usage
patterns (Eick, 2001), and visualizing the structures of a knowledge
domain (C. Chen & Paul, 2001) have been stimulated by the networked
and virtual nature of human society resulting from the adoption of
advanced technologies.
Information visualization is unquestionably an interdisciplinary
research field. It integrates the understanding of domain knowledge and
human visual perception with computer graphics techniques. It also
needs the support of information analysis algorithms (H. Chen et al.,
1998). After a decade of focusing on system development, the lack of
thorough, summative approaches to evaluating existing visualization
systems has become increasingly apparent (C. Chen & Czerwinski,
2000). Special issues of International Journal of Human-Computer
Studies have demonstrated the level of effort being extended to tackle
this issue (C. Chen & Czerwinski, 2000). We believe more disciplines will
contribute to visualization research as the technology moves forward
and application domains expand.
142 Annual Review of Information Science and Technology
A Theoretical Foundation for Visualization
Visualization research is important because the human eye can
process many visual cues simultaneously. For example, humans can
detect a single dark pixel in a 500 x 500 array of white pixels in less than
a second. The display can be replaced every second by another, enabling
a search of 15 million pixels in a minute (Ware, 2000). Also, people have
a truly remarkable ability to recall pictorial images. In one study,
Standing, Conezio, and Haber (1970) showed subjects 2,560 pictures,
each for 10 seconds over seven hours, in a four-day period. Afterward,
subjects were asked to classify pictures presented at a rate of 16 pictures
per second and achieved better than 90 percent accuracy. People identify
patterns through visual aids but may fail to do so when looking a t tables
and numbers. However, the human visual system identifies patterns
according to its own rules. Because patterns will be invisible if they are
not presented in certain ways, understanding visual perception can be
helpful in the design of visualization systems. Ware (2000) surveyed perception studies related to visualization. Believing that the best visualization is one that can help problem solving, Ware (2000) sketched a model
of human memory by synthesizing studies by Card, Moran, and Newel1
(1983);Anderson, Matessa, and Lebiere (1997); Kieras and Meyer (1997);
and Strothotte and Strothotte (1997). According to Ware (ZOOO), the
human memory structure contains iconic, working, and long-term memories, each of which can be enhanced by visualization in a different way,
Iconic memory is the memory buffer where pre-attentive processing operates. This involves a massive number of parallel
processes that extract diverse visual cues for every visual point
on the interface. Incoming visual information stays in iconic
memory for less than a second before part of it is “read out” into
working memory. Pre-attentive processing is important to visualization design because certain visual patterns can be detected
a t this stage without having to go through the cognition
process. The theory of visual processing channels and their
independent status is fundamental to understanding pre-attentive processing (Ware, 2000). Many studies work with this theory to help make an object visually appealing to viewers. Visual
cues such as color and proximity are independent of each other
because they are processed in different visual channels. As
such, they can be empIoyed independently to convey different
attributes. This theory serves as the theoretical foundation for
glyph representation (Chernoff, 1973). Other visual cues such
as color and luminance can interfere with each other because
their visual channels overlap. Gestalt laws (KoMEa, 1935) suggest several ways t o combine independent and related visual
cues to deliver perceivable static patterns. Designing effective
visualizations with computer animation also relies on understanding perception of motion patterns.
Information Visualization 143
Working memory integrates information extracted from iconic
memory with information loaded from long-term memory for
problem solving. Abstract visual patterns perceived by preattentive processing are mapped into patterns of the information space a t this stage. Working memory holds information for
pending tasks; people’s attention decides the space allocated to
a task. Similar to the RAM (random access memory) of a computer, input and intermediate results of an ongoing operation
are stored, but discarded once the task is accomplished.
Visualization can augment the working memory in two ways,
memory extension and visual cognition extension (Ware, 2000).
The high bandwidth of visual input enables working memory to
load external information a t the same speed as loading internal
memory (Card et al., 1983; Kieras & Meyer, 1997). Visualization thus can serve as an external memory, saving space in the
working memory. In addition to memory extension, visualization can facilitate internal computation. Because it makes
solutions perceivable (Zhang, 19971, visualization reduces
the cognitive load of mental reasoning and mental image
construction that is necessary for certain tasks. Interaction
with a visual interface can enhance such cognition extension. The best example is a computer aided design (CAD)
system’s helping an engineer design a product without having to build it.
Long-term memory stores information associated with a lifetime’s experiences. It is not just a repository of information; it
is a network of linked concepts (Collins & Loftus, 1975;Yufik &
Sheridan, 1996). The way this network is built determines
whether certain ideas will be easier t o recall than others. A
sketch of links between concepts is believed to be’an effective
learning aid for students (Jonassen, Beissner, & Yacci, 1993).
Using proximity to represent relationships among concepts in
constructing a concept map has a long history in psychology
(Shepard, 1962). Visualization systems such as Spatial
Paradigm for Information Retrieval and Exploration (SPIRE)
(Wise, Thomas, Pennock, Lantrip, Pottier, Schur, et al., 1995)
and ET Map (H. Chen et al., 1998) also use proximity to indicate semantic relationships among concepts. Those systems
generate from a large collection of text documents a concept
map that can help users better understand the collection that
is depicted.
In summary, visualization augments iconic memory, working memory, and long-term memory in different ways. Psychologists and neuroscientists have conducted many related studies; a complete survey is
beyond the scope of this chapter. Interested readers are referred to Ware
144 Annual Review of Information Science and Technology
(2000). Most perception studies can be helpful to the design of visualization systems, but converting their results to design principles that
can be applied immediately remains a challenge.
Visua I izat ion Classification: Application Focus
Visualization is commonly classified based on application focus.
Categories usually include scientific visualization, software visualization, and information visualization. These categories are not mutually
exclusive and have fuzzy boundaries. For instance, scientific visualization often involves visualizing the multidimensional attribute space of a
physical object; this overlaps with information visualization, which
delivers patterns embedded in large-scale information collections.
Seesoft (Eick, Steffen, & Sumner, 1992) is a system monitoring the
change of software code. It has been discussed in books about both information visualization (Card et al., 1999) and software visualization
(Stasko, Domingue, Brown, & Price, 1998). The abstract nature of input
leads Card et al. (1999) t o regard both software visualization and information visualization as information visualization.
Scientific Visualization
Scientific visualization helps scientists and engineers more efficiently
understand physical phenomena embedded in large volumes of data
(Nielson, 1991). The data may come from complex simulation models or
from sensors such as satellites, medical scans, or telescopes. What distinguishes scientific visualization is the fact that it is always about physical objects. This condition provides natural counterparts such as the
earth, the human body, the molecule, DNA, or an airplane to which the
information can be mapped. Developing mathematical models t o
describe physical objects plays an essential role in mapping information.
Colors or other visual cues are usually added to a physical object to
describe different attributes. Isosurfaces, volume rendering, and glyphs
are commonly used techniques for the description of attributes in scientific visualization. Isosurfaces depict the distribution of certain attributes. One example is the use of color contours t o convey temperature
distribution over a map. Volume rendering allows viewers to see the
entire volume of 3-D data in a single image (Nielson, 1991). The 3-D data
may come from medical magnetic resonance imaging (MRI), CAD, or
remote sensing. Interaction between a visual display and its viewers
directly affects the effectiveness of a volume-rendering visualization.
Glyphs provide a way to display multiple attributes through combinations of various visual cues (Chernoff, 1973). Scientific visualization typically uses glyphs t o describe flow information. A commonly used glyph
is an arrow (Fayyad, Grinstein, & Wierse, 2002). A map with arrows representing magnitude and direction of wind a t a place suggests the movement of air over a geographical area.
Information Visualization 145
In addition to displaying distributions of attributes over a physical
object, scientists and engineers also need visual aids to describe relationships among abstract attributes. Techniques used to visualize some
of these attributes overlap with those used in information visualization
and are discussed in the next subsection.
Software Visualization and Informa tion Visualization
Unlike scientific visualization, software visualization and information visualization usually do not have inherent geometries by which to
map information. They share approaches t o representing abstract information on a computer screen. For instance, the TreeMap representation
has been used to represent a hierarchical relationship in software (Jeffery,
1998), financial data (http:llwww.smartmoney.cordmarketmap),and Usenet messages (Smith & Fiore, 2001). However, each visualization type has
its own application focus.
Software visualization helps people understand and use computer
software effectively (Stasko et al., 1998). Generally two types of software
visualization are used, program visualization and algorithm animation.
Program visualization, also positioned as a subfield of software engineering, helps programmers manage complex software (Baecker &
Price, 1998). For instance, the Microsoft Windows 95 system has ten million lines of code, for which maintenance can be expensive. Program
visualization tackles this problem by visualizing the source code
(Baecker & Marcus, 1990), the data structure employed, changes made
to the software (Eick et al., 1992), and run-time performance. Program
visualization can be an effective tool for software maintenance, understanding, optimization, and debugging. Algorithm animation, on the
other hand, is mainly used for education. Starting with the movie
Sorting Out Sorting (Baecker, 1981), various algorithm animation systems have been developed to motivate and support the learning of computational algorithms.
Information visualization helps users identify patterns, correlations,
or clusters. The information visualized can be structured or unstructured. Structured information, usually in numerical format, has welldefined variables. Examples include business transaction data, Internet
traffic data, and Web usage data. Visualization of this type focuses on
graphical representation to reveal patterns. Early on, standard, static
graphics such as line graphs, scatter plots, bar charts, or pie charts were
used to enhance understanding of stored data. Widely used commercial
tools including Spotfire (http:llwww.spotfire.com), SAS/GRAPH (http:/l
www.sas.cordtechnologiesh~query-reporting/graph),
SPSS (http://spss.
com), ILOG (http://www.ilog.com), and Cognos (http://www. cognos.com)
offer interactive visualizations t o help users gain value from structured
information.
The recent integration of this type of visualization with various data
mining techniques has attracted attention, as huge volumes of data are
146 Annual Review of Information Science and Technology
routinely being generated and stored in databases. Computerized visualizations are vehicles for the delivery of patterns or structures identified by data mining algorithms. Without visualization, such patterns or
structures might be too complex to be understandable (Fayyad et al.,
2002). Interaction between the visualization system and the user also
permits the inclusion of human expertise or feedback in data mining,
leading t o more effective data exploration. At the same time, data mining algorithms serve as preprocessors, finding appropriate perspectives
and dimensions for visualization. Stronger interaction between visualization and data mining algorithms can be found in the systems of
Thearling, Becker, and Decoste (2002) and Johnston (2002) where data
mining models are visualized to help users understand back-end algorithms. Such interaction is usually employed to facilitate computational
steering, a process defined as the ongoing intervention of users in the
execution of an otherwise independent computational process (Parker,
Johnson, & Beazley, 1997). Incorporating users’ skill and expertise, the
computational steering approach may improve the efficiency and performance of a data-mining tool.
Unstructured information, on the other hand, usually does not have
well-defined variables. Examples of unstructured information include a
collection of office documents, a collection of Web sites, or an e-mail
archive. Unlike the visualization of structured information, this type of
application often needs to identify variables (e.g., titles, locations, subject keywords) and to construct visualizable structures before the graphical representation. Several commercial visualization systems, including
Vantage Point (http://www.thevantagepoint.com),SemioMap (http:l/
www.entrieva.com/entrieva), and Knowledgist (http://www.inventionmachinexom), have applied different information analysis technologies
to understand the semantics of unstructured information.
In summary, software visualization and information visualization
transform data and map information into a visual space differently. But
both use similar metaphors to represent abstract information and they
adopt similar techniques for user-computer interactions. The next section provides detailed descriptions of those representation and interaction approaches.
A Framewo rk for Inf ormat ion Visua Iizat ion TechnoIogies
Previous studies have constructed various taxonomies to categorize
visualization research from different perspectives. Chuah and Roth
(1996) list the tasks of information visualization, and Bertin (1967) and
Mackinlay (1986) describe the characteristics of basic visual variables
and their applications to different data types. Card and Mackinlay
(1997) expand the research of Bertin (1967) and Mackinlay (1986) by
constructing a data type-based taxonomy. Based on the features of data
domains, the taxonomy divides the visualization field into several categories: scientific visualization, geographic information systems (GIs),
Information Visualization 147
multidimensional tables, information landscapes and spaces, nodes and
links, trees, and text. Although a taxonomy based on data type may help
the implementer select appropriate visualization technologies, the taxonomy Chi (2000) has proposed indicates how to apply these technologies. Chi (2000) breaks the visualization data pipeline into four distinct
stages: value, analytic abstraction, visual abstraction, and view.
Visualization techniques are thus classified based on the data stage at
which they are applied. Chi (2000) contends that there are three types
of techniques for transforming data from one stage to the next and four
types of technology operating within each stage. The technologies
applied at the early data stages extract or construct visualizable structures; those applied at later stages convert these structures into visual
metaphors and provide appropriate user-interface interactions. Chi
(2000) surveys thirty visualization systems and lists the technologies
they apply at different data stages.
Because the development of a visualization system usually integrates
several techniques, it may be helpful to provide a framework of visualization technologies based on their functionalities. Shneiderman (1996)
identified two aspects of visualization technology that can be directly
applied to a given structure. One focuses on mapping abstract information to a visual representation and the other provides user-interface
interactions for effective navigation over displays on a screen. To fulfill
users’ requirements, visualization systems usually combine techniques
from these two aspects. However, as indicated by C. Chen (19991, when
it comes to visualizing unstructured or high-dimensional information,
another set of technologies is needed to create structures that characterize the data set. Along with representation and user-interface interaction, information analysis technology also helps support a
visualization system. It serves as a preprocessor, deciding what is to be
displayed on a computer screen. Such automatic preprocessing becomes
especially critical when manual preprocessing is not ’ possible. The
remainder of this section reviews the three research dimensions that
support the development of an information visualization system: information representation, user-interface interaction, and information
analysis. The framework described in this section is consistent with
Chi’s (2000) taxonomy, but focuses more on the characteristics of technologies available in each dimension.
information Representation
Shneiderman (1996) proposed seven types of representation methods:
the 1-D, 2-D, 3-D, multidimensional, tree, network, and temporal
approaches. We use this framework to review related research.
9
The 1-D approach represents abstract information as onedimensional visual objects and displays them on the screen in
a linear (Eick et al., 1992; Hearst, 1995) or a circular (Salton,
148 Annual Review of Information Science and Technology
Allan, Buckley, & Singhal, 1995) manner. Representation in 1D has been used to display either the contents of a single document (Hearst, 1995; Salton et al., 1995) or to provide an
overview of a document collection (Eick et al., 1992). Colors
usually represent some attributes of each visual object. For
instance, colors indicate document type in the SeeSoft system
(Eick et al., 1992) and depict the location in a document of
search terms in TileBars (Hearst, 1995).A second axis may also
play a role, presenting some characteristic of each visual object.
One example is the SeeSoft system that piles up documents on
the x-axis and uses the y-axis to visualize the number of lines
in each document. Figure 4.1' displays an interface from the
TdeBars system that shows the occurrence of search terms in
documents. The darkness of each tile indicates the frequency of
a search term in a document.
Figure 4.1
TileBars uses a l - D approach to show term-document
relevance (http:llwww.acm.org/sigchilchi95lElectronicldocumnts/
paperslmahPfg4.gif, 0 1995 ACM, Inc.). Figure available in color
at http:llwww.asis.orglPublicationslARISTlvol39ZhuFigures.html
Information Visua I izat ion 149
A 2-D approach represents information a s two-dimensional
visual objects. Visualization systems based on 2-D output of a
self-organizing map (SOM) (Kohonen, 1995) belong to this category. Such systems display categories created over a large collection of textual documents, with the layout of each category
based on its location in the two-dimensional area of the SOM.
Spatial proximity on the interface represents the semantic
proximity of the categories created. The challenge in this
approach is to help users deal with the large number of categories that will have been created for the mass textual data.
A 3-D approach represents information as three-dimensional
visual objects. One example is the WebBook system (Card,
Robertson, & York, 1996) that folds Web pages into threedimensional books. Realistic metaphors such as rooms (Card et
al., 1996), bookshelves (Card et al., 1996), or buildings
(Andrews, 1995) are employed to depict abstract information.
Visualization systems using a 3-D version of a tree or network
representation also belong to this category. One example is the
3-D hyperbolic tree created by Munzner (2000) to visualize
large-scale hierarchical relationships. Figures 4.2 and 4.3 show
screenshots of WebBook and WebForager, respectively, where
the book metaphor is applied to organize Web pages from the
same Web site and the WebForager provides a workspace t o
place books in use.
The multidimensional approach represents information as multidimensional objects and projects them into a three-dimensional
Figure 4.2
The WebBook (http://acm.org/sigchi/chi96lproceedings/papers/
Cardlskcltxt.htm1, 0 1996 ACM, Inc.). Figure available in color
at http://www.asis.org/Publications/ARIST/vol39~huFigures.html
150 Annual Review of Information Science and Technology
Figure 4.3
The WebForager (http://www.acm.org/sigchi/chi96/proceedings/
papers/Card/skcltxt.htrnl,0 1996 ACM, Inc.). Figure available in
color at http://www.asis.org/Publications/ARlST/vol39Zhu
Figureshtrnl
or a two-dimensional space. This approach often represents textual documents a s a set of key terms that identify the theme of
a textual collection. A dimensionality reduction algorithm, such
as multidimensional scaling (MDS), hierarchical clustering, Kmeans algorithms, or principle components analysis, is used to
project document clusters or themes that have been sorted into
a two-dimensional or three-dimensional space. The SPIRE system presented in Wise et al. (1995) and the VxInsight system
(Boyack, Wylie, & Davidson, 2002) belong to this category.
Figures 4.4 and 4.5 display two types of visualization developed
for the SPIRE system. The Galaxy (Figure 4.4) clusters 567,437
abstracts of cancer literature based on the semantic similarity;
the Themeview (Figure 4.5) visualizes relationships among topics of a document collection. Glyph representation, another type
of multidimensional representation, uses graphical objects or
symbols to represent data through visual parameters t h a t are
spatial (positions x or y ) , retinal (color and size), or temporal
(Chernoff, 1973). I t has been used in various social visualization
techniques (Donath, 2002) to describe human behavior during
computer-mediated communication (CMC).
9
The tree approach is often used to represent hierarchical relationships. The most common example is a n indented text list.
Other tree structure systems include the Tree-Map (Johnson &
Shneiderman, 19911, t h e Cone Tree system (Robertson,
Mackinlay, & Card, 19911, and the Hyperbolic Tree (Lamping e t
Information Visualization 151
Figure 4.4
Galaxy visualization of text documents (http://www.pnl.gov/
infoviz/galLcancer800.gif, reprinted with permission). Figure
available in color at http://www.asis.org/Publications/ARIST/
vol39ZhuFigures.html
al., 1995). One crucial challenge to this approach is that the
number of nodes grows exponentially as the number of tree levels increases. As a consequence, different layout algorithms
have been applied. For instance, the Tree-Map (Johnson &
Shneiderman, 1991) allocates space according to attributes of
nodes, while the Cone Tree (Robertson et al., 1991) takes
advantage of the 3-D visual structure to pack more nodes on the
screen. Figure 4.6 displays the visual interface of the Cat-aCone system (Hearst & Karadi, 1997) that applies the 3-D Cone
Tree to visualize hierarchies in Yahoo!. The Hyperbolic Tree
(Lamping et al., 1995), on the other hand, projects subtrees on a
hyperbolic plane and puts the plane into the range of display. A
3-D version of the hyperbolic tree has also been developed by
Munzner (2000) to visualize large-scale hierarchies (Figure 4.7).
The network representation method is often applied when a
simple tree structure is insufficient for representing complex
relationships. Complexity is evident, for example, in citations
152 Annual Review of Information Science and Technology
Figure 4.5
ThemeView.The height of a peak indicates the strength of a
given topic in the collection of documents (http://www.pnl.gov/
inofviz/theme~cnn800.gif,reprinted with permission). Figure
available in color at http://www.asis.org/Publications/ARIST/
vol39ZhuFigures.html
among academic papers (C. Chen & Paul, 2001; Mackinlay,
Rao, & Card, 1995) or among textual documents that are distributed over, and linked by, the Internet (Andrews, 1995).
Various network visualizations have been created to represent
citation relationships (Mackinlay e t al., 1995) or to display the
World Wide Web (Andrews, 1995). The spring-embedder model,
originally proposed by Eades (1984), along with its variants
(Davidson & Harel, 1996; Fruchterman & Reingold, 19911,
have become the most popular drawing algorithms €or network
relationships. Figure 4.8 presents the visualization of coauthorship among 555 scientists using a spring-embedder
equivalent algorithm.
The temporal approach visualizes information based on temporal order. Location and animation are two commonly used
visual variables to reveal the temporal aspect of information.
Visual objects are usually listed along one axis according to the
Information Visualization 153
Figure 4.6
Cat-a-Cone tree that displays hierarchies in Yahoo!. The label of
a node can be brought to the foreground with a click (http://
www.sims.berkeley.edu/-hearst/cac-overview.html,O 1997
ACM, Inc.). Figure available in color at http://www.asis.org/
Publications/ARIST/vol39ZhuFigures.html
time when they occurred, while the other axis may be used to
display the attributes of each temporal object (Eick et al., 1992;
Robertson et al., 1993). For instance, the Perspective Wall
(Robertson et al., 1993) lists objects along the x-axis based on
time sequence and presents attributes along the y-axis. Using
animation is another way to display temporal information. In
the VxInsight system (Boyack et al., 20021, the landscape
changes its appearance as a user chooses a different point of
time on a time-slider.
The seven types of representation methods turn abstract textual documents into objects that can be displayed. A visualization system usually
applies several methods at the same time. For instance, there are 2-D
hyperbolic trees and 3-D hyperbolic trees. The multilevel ET map created by H. Chen et al. (1998) combines both 2-D and the tree structure,
where a large set of Web sites is partitioned into hierarchical categories
based on the sites’ content. The entire hierarchy is organized in a tree
154 Annual Review of Information Science and Technology
Figure 4.7
A 3-D hyberbolic space (http://graphics.stanford.edu/papers/
munzner-thesislhyp-figshtml, reprinted with permission of
Tamara Munzner). Figure available in color at http://www.asis.
org/Publications/ARIST/vol39ZhuFigures.html
structure, and each node in the tree is a two-dimensional SOM on which
the subcategories are displayed graphically.
Some representation methods also need to have a precise information
analysis technique at the back end. For instance, the TileBar system
(Hearst, 1995) employs a text-tiling analysis algorithm to segment a document; alternatively, the Themeview and Galaxy (Wise et al., 1995) use
multidimensional scaling to cluster and lay out documents on the screen.
The “small screen problem” (Robertson et al., 1993) is common to representation methods of any type. To be effective, a representation
method needs to be integrated with the user interface. Recent advances
in hardware and software allow rapid user-interface interaction, and
various combinations of representation methods and user interface
interactions have been employed. For instance, Cone Tree (Robertson et
al., 1991) applies 3-D animation to provide direct manipulation of visual
Information Visualization 155
Figure 4.8
Visualization of a large-scale co-authorship network (http://mpifg-koeln.mpg.de:80/-lk/netvis/Huge.html, reprinted with permission of Lothar Krempel). Figure available in color at http://www.
asis.org/Publications/ARlST/vol39ZhuFigures.html
objects, and Lamping et al. (1995) integrate hyperbolic projection with
the fish-eye view technique to visualize a large hierarchy.
User-Interface Interaction
Immediate interaction between a n interface and its users not only
allows direct manipulation of the visual objects displayed, but also
allows users to select what is to be displayed and what is not (Card et
al., 1999). Shneiderman (1996) summarizes six types of interface functionality: overview, zoom, filtering, details on demand, relate, and history. Techniques have been developed to facilitate various types of
interactions and this subsection briefly reviews the two most commonly
used interaction approaches: overview + detail and focus + context (Card
et al., 1999).
Overview + detail provides multiple views, with the first being a n
overview: providing overall patterns to users. Details about the part of
interest to the user can be displayed. These views may be displayed a t
the same time or separately. When a detailed view is needed, two types
of zooming are usually involved (Card et al., 1999): spatial zooming and
semantic zooming. Spatial zooming refers to the process of enlarging
156 Annual Review of Information Science and Technology
selected visual objects to obtain a closer look, whereas semantic zooming
provides additional information about a selected visual object by changing its appearance.
The focus + context technique provides detail (focus)and overview (context) dynamically on the same view. One example is the 3-D perception
approach adopted by systems such as Information Landscape (Andrews,
1995) and Cone Tree (Robertson et al., 1991), where visual objects at the
front appear larger than those at the back. Another commonly used focus
+ context technique is the fish-eye view (Furnas, 1986), a distortion technique that acts like a wide-angle lens to amplify part of the display. The
objective is t o simultaneously provide neighboring information in
reduced detail and supply greater detail on the region of interest. In any
focus + context approach, users can change the region of focus dynamically. A system that applies the fish-eye technique is the Hyperbolic Tree
(Lamping et al., 1995), in which users can scrutinize the focus area and
scan the surrounding nodes for the big picture. Other focus + context
techniques include filtering, highlighting, and selective aggregation
(Card et al., 1999).
Overview + detail and focus + context are the two types of interaction
usually provided by a visualization system to help users deal with large
volumes of information.
In formation Analysis
Confronted with large quantities of unstructured information, an
information visualization system needs to apply information analysis to
reduce complexity and to extract salient structure. Such an application
often consists of two stages, indexing and analysis.
The indexing stage aims to extract the semantics of information to
represent its content. Different preprocessing algorithms are needed for
different media types, including text (natural language processing),
image (color, shape, and texture-based segmentation), audio (indexing
by sound and pitch), and video (scene segmentation). This subsection
briefly reviews selected approaches to textual document processing.
Automatic indexing (Salton, 1989) is a method commonly used to represent the content of each document as a vector of key terms. When
implemented using multiword (or multiphrase) matching (Girardi &
Ibrahim, 1993), a natural language processing noun-phrasing technique
can capture a rich linguistic representation of document content ( h i c k
& Vaithyanathan, 1997). Most noun phrasing techniques rely on a combination of part-of-speech-tagging (POST) and grammatical phraseforming rules. This approach has the potential to improve precision over
other document indexing techniques. Examples of noun-phrasing tools
include the Massachusetts Institute of Technology’s Chopper, Nptool
(Voutilainen, 1997),and the Arizona Noun Phraser (Tolle & Chen, 2000).
+formation extraction is another way to identify useful information
from text documents automatically. It extracts names of entities of
Information Visualization 157
interest, such as persons (e.g., “John Doe”), locations (e.g., “Washington,
D.C.”), and organizations (e.g., “National Science Foundation”) from textual documents. It also identifies other entities, such as dates, times,
number expressions, dollar amounts, e-mail addresses, and Web
addresses (URLs). Such information can be extracted based on either
human-created rules or statistical patterns occurring in the text. Most
existing information extraction approaches combine machine learning
algorithms such as neural networks, decision trees (Baluja, Mittal, &
Sukthankar, 1999), hidden Markov models (Miller, Leek, & Schwartz,
1999), and entropy maximization (Borthwick, Sterling, Agichtein, &
Grishman, 1998) with a rule-based or a statistical approach. The best
systems have been shown to achieve more than 90 percent accuracy in
both precision and recall rates when extracting persons, locations, organizations, dates, times, currencies, and percentages from a collection of
New York Times articles (Chinchor, 1998).
At the analysis stage, classification, and clustering are commonly
used to identify embedded patterns. Classification assigns objects into
predefined groups (using supervised learning), whereas clustering
aggregates objects dynamically based on their similarities (unsupervised learning). Both methods generate groups by analyzing characteristics of objects extracted a t the indexing stage. Widely used
classification methods include the naive Bayesian method (Koller &
Sahami, 1997; Lewis & Ringuette, 1994; McCallum, Nigam, Rennie, &
Seymore, 1999), k-nearest neighbor (Iwayama & Tokunaga, 1995;
Masand, Linoff, & Waltz, 1992), and network models (Lam & Lee, 1999;
Ng, Goh, & Low, 1997; Wiener, Pedersen, & Weigend, 1995).
Unlike classification, clustering determines groups dynamically. A
commonly used clustering algorithm is Kohonen’s self-organizing map,
which produces a two-dimensional grid representation for N-dimensional features and has been widely applied in information retrieval
(Kohonen, 1995; Lin, Soergel, & Marchionini, 1991; Omig, Chen, &
Nunamaker, 1997). Other popular clustering algorithms include multidimensional scaling, the k-nearest neighbor method, Ward’s algorithm
(Ward, 19631, and the K-means algorithm.
Information analysis represents each textual document with semantically rich phrases or entities (indexes) and identifies interesting patterns by using classification and clustering algorithms. Supporting a
visualization system with these methods of analysis enables the system
to deal with larger and more complex collections of information.
Emerging Information Visualization Applications
Information visualization can be applied to any domain where people
need to extract insights from a vast amount of information. This is evidenced by the publication of several new books. Bederson and
Shneiderman (2003) document various applications of visualization
developed a t the University of Maryland; Borner and Chen (2003) record
158 Annual Review of Information Science and Technology
different visualization applications in the development of digital
libraries. In addition, C . Chen (1999) describes many visualization applications in virtual environments. This section explores various
approaches to building visualization systems in the domains of digital
libraries, the Web, and virtual communities, where large amounts of
information are routinely generated.
Digital Library Visualization
Digital library research aims a t enhancing information collection by
facilitating access to, and the exploration of, stored information. A digital library may contain millions of objects including journal papers,
books, maps, photographs, films, videos, and audio recordings. Because
standard search engine techniques are no longer sufficient for accessing
information in digital libraries, visualization can be applied to support
both the browsing and the searching activities of users.
Browsing a Digital Library
Browsing is a way to retrieve information when a user does not have
a specific goal (H. Chen et al., 1998; Marchionini, 1987). Visualization
supports browsing by providing an effective overview that summarizes
the contents of a collection. Interaction techniques are employed t o lead
a user to information of interest.
Providing a subject hierarchy is a conventional way to help browse
information in a digital library. For example, MEDLINE, the largest and
most widely used medical bibliographic database in the world, utilizes
the vocabulary of the Medical Subject Headings (MeSH) to index its textual documents manually and organizes MeSH terms into 15 hierarchies
called the MeSH tree structures (Lowe & Barnett, 1994).A user can traverse the MeSH tree to locate appropriate medical terms. Such a largescale subject hierarchy can readily become unmanageable because users
can easily become lost when scrolling through the headings (Lowe &
Barnett, 1994).
Several visualization systems have been developed to display this
large-scale hierarchy more effectively. The MeSHBROWSE system
(Korn & Shneiderman, 1995) enables users to browse a subset of the
MeSH tree interactively. Subcategories for a selected category are displayed, but the two-dimensional tree representation employed suffers
from the problem of limited space. To utilize the space on a computer
screen more effectively, Hearst and Karadi (1997) proposed using a
three-dimensional Cone Tree and animation to display the MeSH tree.
However, being able to display a large-scale hierarchy is not enough.
Both the MeSHBROWSE system and the 3-D Cone Tree rely on a MeSH
tree that is manually generated. This approach cannot be adopted for
other digital libraries unless there is an existing subject hierarchy. In
addition, manual generation of a subject hierarchy is not only expensive
but also too slow to catch emerging topics in a timely fashion.
Information Visualization 159
Figure 4.9
The Interface of CancerMap. Category “Liver Neoplasms” was
selected at the top level and the submap of “Liver Neoplasms”
was displayed. Figure available in color at http://www.asis.org/
Pub1ications/ARIST/vol39Zhufigures. html
160 Annual Review of Information Science and Technology
The CancerMap system described by H. Chen, Lally, Zhu, and Chau
(2003) adopted the SOM and Arizona Noun Phraser (Tolle & Chen, 2000)
approaches to generate a subject hierarchy automatically. Figure 4.9 presents two consecutive screen shots, displaying the top-level categories
and subcategories under the category of “Liver Neoplasms.” The empirical study described by H. Chen et al. (2003) indicates that this approach
generated a meaningful subject hierarchy to supplement or enhance
human-generated hierarchies in digital libraries. The interface applies
the overview + detail approach by combining the 2-D display of SOM with
a l-D text-based alphabetic display. Such a combination appears t o be a
promising approach to visualizing large-scale subject hierarchies (Ong,
Chen, Sung, & Zhu, in press). Users can find a correct path systematically when using a l-D display. The 2-D SOM map also provides more
visual cues and delivers richer information about each node within a
hierarchy by using spatial location to illustrate semantic relationships
among categories (size for the number of documents within a category
and color for the number of levels beneath a category). These features
allow easy comparison of categories on the same level. It appears that the
best strategy for using the interface is to use the l-D display for path
management when traversing a hierarchy and to use the 2-D SOM map
to compare categories at the same level (Ong et al., in press).
In addition to subject hierarchy, other approaches to support browsing behavior have been proposed. If all documents are geo-referenced,
users may browse a digital library by geographical locations. A clickable
geographic map can serve as an overview (Cai, 2002). Christoffel and
Schmitt (2002) built a virtual-reality interface to simulate a real-world
library, aiming to provide an environment that would be familiar to
users, who could navigate the interface as if walking in a library.
Searching a Digital Library
When a user has a specific goal, searching rather than browsing is
often the preferred mode of interaction. Visualization can support
searching behavior in two ways: query specification and search results
analysis.
Providing a subject hierarchy not only facilitates browsing but also
suggests appropriate query terms for searching. Users can combine the
terms in the hierarchy to specify their queries. Visualization approaches
to providing an overview may also be applied to help users organize
search results. For instance, H. Chen, Chau, and Zeng (2002) used
dynamic SOM to categorize search results based on content. Other visualization systems such as VIBE (Olsen, Korfhage, & Sochats, 1993) and
TileBars (Hearst, 1995) provide visual cues to indicate the extent of
match between a document returned and a query term. The VIBE system
displays both documents and search terms, with the spatial distance
between a document and a term indicating their semantic relationshipt h i shorter the distance the stronger the relationship. TileBars, on the
other hand, uses grayscale colors to indicate the frequency of search
Information Visualization 161
terms in a document (Figure 4.1). Visualization can also help users maintain
their search results. For example, in Hearst and Karadi (1997), the system
organizes documents returned into a book, with the book cover showing the
search terms, thereby helping store and manage search results (Figure 4.4).
With the proliferation of digital library content and services, we
believe that visualization can significantly enhance the value of a digital library by facilitating browsing and searching.
Web Visualization
The vast Web information space has probably become the most dominant information and communication resource for both academic
researchers and the general public. Its rapid growth and constant changes
also have posed a formidable challenge to visualization research.
Involving both academic and commercial efforts, Web visualization
aims to provide a more effective way to access and maintain the Web.
Two types of Web visualization, visualization of a single Web site and
visualization of a collection of Web sites, will be discussed in the remainder of this subsection.
Visualization of a Single Web Site
The structure of a Web site can be visualized to provide “table of contents” information for effective Web site surfing and maintenance. Most
sites have site maps for this purpose, but designing an effective graphical site map remains challenging, especially when a site may contain
thousands of pages. A tree metaphor is commonly used to represent the
hierarchical structure of a Web site. Visualizations such as the StarTree
by InXight Software (http://www.inxight.com), the SiteBrain by Brain
Technologies Corporation (http://mappa.mundi.net), and the Z-factor
site map of Dynamic Diagrams (http://www.dynamicdiagrams.com)all
employ a tree representation but differ in the type of tree used. Visual
cues such as color, shape, or icon are applied to describe the attributes
of a tree node in the hierarchy. The attributes may include the title of a
page, the status of a page (the date of latest update), the type of page
(text or image), or usage. A visualization system selects certain attributes for display based on the intended functionality. It may also link
nodes with arrows in the tree to describe traffic direction within a Web
site (Cugini & Scholtz, 1999). Eick (2001) describes several visual interfaces, all of which use the hyperbolic tree + fish-eye view approach. One
interface assigns labels to nodes to construct a site map; another represents a node with a 3-D vertical line indicating the usage of the Web
page. In addition, Chi, Pitkow, Mackinlay, Pirolli, Gossweiler, and Card
(1998) used several Cone Trees along the x-axis in chronological order to
depict the temporal evolution of a Web site. Each Cone Tree presents the
usage pattern of the site over a four-week period, with colors to describe
the usage of each Web page. Figure 4.10 shows an example of Web site
visualization for a company (bestbuy.com)based on a hyperbolic tree.
162 Annual Review of information Science and Technology
Figure 4.10
A graphical site map. StarTree (by InXight), which applies
hyperbolic tree and fish-eye view algorithms, was used to
visualize a Web site’s structure. Colors were used to distinguish sub-trees (http://inxight.com/products/oem/star-tree/
demos.php). Figure available in color at http://www.asis.org/
Publications/ARIST/vol39ZhuFigures.html
Most existing visualization systems for a single Web site apply a tree
representation and use visual cues to describe each page, relying on
users to identify patterns. The challenge faced with this type of visualization is the same as that faced by the tree representation: how can a
very large-scale tree be displayed on a computer screen in an understandable way. Almost no information analysis technology is involved
because the tree metaphor appears to be a natural representation of the
hierarchical structure of a Web site. However, as Web log analysis
becomes more popular for understanding online behavior, visualization
of a single Web site may need to apply information analysis technology
Information Visualization 163
to identify and display patterns embedded in the Web log data. Those
patterns may include user demographics, browsing behaviors, and
online purchases.
Visualization for a Collection of Web Sites (and Web Pages)
The common goal for visualizing collections of Web sites is to support
information exploration over the Internet. Systems like ET map (H.
Chen et al., 1998) organize Web pages based on content, applying the
output of a self-organizing map to project categories. Other visualization
systems organize cyberspace based on the link structure among Web
pages (Andrews, 1995; Bray, 1996). Three-dimensional icons are presented on a two-dimensional map, where each icon represents a single
Web page or a Web site. The mapping of 3-D icons is based on predefined
hierarchical categories (Andrews, 1995) or on the strength of linkages
among Web sites (Bray, 1996). Visual cues are supplied to each 3-D icon
to represent attributes including size, type, number of incomingloutgoing links, and title of the Web page or Web site. Intensive computation
is usually conducted to preprocess Web pages before visualization. ET
Map (H. Chen et al., 1998) used automatic indexing to represent the content of a Web page and SOM to generate the subject hierarchy. Bray
(1996) calculated links among Web sites to measure the “visibility”
(number of links pointing to the site) and the “luminosity” (number of
outgoing links) of each Web site.
With the ever-increasing quantity of Web sites and Web content, Web
visualization promises to be a fertile ground for information visualization research.
Virtual Community Visualization
The Internet not only opens the door to information foraging but also
offers new communication media such as e-mail, discussion groups,
news groups, and chat rooms. These new media facilitate communication across geographical and time boundaries, stimulating the formation
of virtual communities or new social networks centered on common
interests and beliefs. The archives of communication contain rich information about discussion content and participant behavior, information
that can be processed and displayed. The proliferation of computermediated communication and online communities inevitably poses challenges to people trying to locate a particular person or community,
retrieve useful information from an archive, or manage their own communication archives. Many visualization systems have been developed
to cope with these issues.
Visualization systems in this area generally belong to one of two categories: tools for communication management and tools for community
analysis. ContactMap (Whittaker, Jones, & Terveen, 2002) and Chat
Circles (Donath, Rarahalios, & Viega, 1999) belong to the first category.
The ContactMap system acts like a visual address book with all contacts
164 Annual Review of Information Science and Technology
displayed on the computer screen as icons. An icon contains a picture
and a name. A user can assign a n icon to one or more predefined groups
and that icon is mapped on the screen according to its groups.
Interactions with a contact can be retrieved by a click on its icon. While
ContactMap helps people manage their social networks, the Chat
Circles system helps users form subgroups in a chat room. It assigns
each user a colored circle enclosing text. The user needs to move his or
her own circle closer to another circle in order to “speak to” and “hear”
t h a t person. Chat Circles 2 offers the capability of tracing the path of a
circle in a chat room. Figure 4.11 presents a screen shot of Chat Circles
2 where the local user is “media lab.” Hollow circles represent other people far away from the local user and semi-transparent, faded circles
show the traces of people who have chatted on that spot before.
Figure 4.1 1
Interface of Chat Circles 2 (http:llchatcircles.media.mit.edu,
reprinted with permission of Judith Donath). Figure available in
color at http:llwww.asis.orglPublicationslARIST/vol39Zhu
Figures.html
Information Visualization 165
Both ContactMap and Chat Circles facilitate communication within a
community, but users may also need help to identify and to understand
a community. Visualization systems such as the Loom (Donath et al.,
1999),Conversation Map (Sack, ZOOO), Netscan Dashboard, and Netscan
Treemap visualize the Usenet, the most popular discussion space on the
Internet. Both the Loom and Conversation Map apply information
analysis technology before visualization. The Loom system uses 2-D representation to describe the temporal patterns of postings in Usenet.
Messages are mapped according to the sender and the time of posting. A
rule-based algorithm is applied to classify messages into four categories:
angry, peaceful, informational, and other. Conversation Map depicts a
community by displaying its social and semantic relationships using the
network metaphor. Information analysis techniques are applied to construct a semantic network. Message structure and quotation analysis
are employed for constructing the social networks.
As part of the Netscan project in Microsoft Research, both Netscan
Dashboard and Netscan Treemap use tree representation to describe different aspects of online discussion groups. Netscan Dashboard employs
a conventional 2-D tree structure to display the hierarchical structure of
a thread, while Netscan Treemap uses Treemap (Shneiderman, 1994) to
present hierarchical relationships among Usenet newsgroups. These
relationships can be inferred from the name of a newsgroup; the size of
a node corresponds with the number of postings in a group.
PeopleGarden (Xiong & Donath, 1999) uses glyphs to summarize the
social activity of a community. A flower metaphor is used to represent
participants, with the number of petals representing the number of postings by the participant and the height of the flower conveying the length
of time that the individual stays. As a community becomes a garden, the
overall activity of this community can be seen a t a glance.
CommunicationGarden combines just such a floral representation with
SOM t o describe the liveliness of each subtopic within a community and
to help locate the most active persons in a certain area. Active participants may not be the most knowledgeable, but will probably be the most
helpful. Figures 4.12a, b, and c display the visualization components of
the CommunicationGarden system: Content Summary (Figure 4.12a),
Interaction Summary (Figure 4.12b), and Expert Indicator (Figure
4.12~).Each type displays a certain aspect of a computer-mediated communication process. In addition, Content Summary, Interaction
Summary, and Expert Indicator divide their display panels into subgardens based on the output of SOM and the Arizona Noun Phraser. Thus
each subgarden represents one subtopic.
Evaluation Research for Information Visualization
In spite of a decade of innovative visualization systems development,
evaluation research for information visualization is still at an early
stage ( C . Chen & Yu, 2000). Our literature survey identified two types of
166 Annual Review of Information Science and Technology
Figure 4.1 2a
Content Summary. The x-axis represents time; categories
generated by the SOM are laid vertically. Each dark line
represents one message. The vertical thickness of each
subtopic indicates its activity on a particular day. The length in
the x-dimension of each subtopic represents the time duration
of that subtopic. Figure available in color at http://www.asis.
org/Publications/ARlST/vol39ZhuFigures.
html
empirical study in information visualization: 1)empirical usability studies that aim to understand the pros and cons of specific visualization
designs or systems, and 2) fundamental perception studies t h a t try to
investigate basic perceptual effects of certain visualization factors or
stimuli. As the consequence of both the diversity of visualization systems and the relative novelty of computer-based visualization, stringent
metrics-based evaluations such as those adopted in TREC (Text
REtrieval Conference) or MUC (Message Understanding Conference)
(Chinchor, 1998) are nonexistent.
Empirical Usability Studies
Most empirical usability studies employ laboratory experiments to
validate the performance of visualization systems and designs, for example, comparing a glyph-based interface and a text-based interface (Zhu
& Chen, 2001), comparing different visualization techniques (Stasko,
Catrambone, Guzdial, & McDonald, ZOOO), or studying a visualization
system in a working environment (Graham, Kennedy, & Hand, 2000;
Pohl & Purgathofer, 2000).
Information Visualization 167
Figure 4.12b
Interaction Summary. The panel is divided into subgardens
based on the SOM output. Each subgarden is a subtopic. Each
flower represents one thread, where the number of petals
represents the number of messages posted for the thread the
number of leaves represents the number of participants in the
thread and the height of the flowers represents the time
duration of the thread. Figure available in color at http://www.
asis.org/Publications/ARIST/vol39ZhuFigures.html
Studies such as Stasko e t al. (2000),Graham et al. (2000), Morse and
Lewis (2000), and Zhu and Chen (2001) use simple but basic visual
operations for evaluation. Sometimes referred to as the “de-featuring
approach,” these studies examine generic operations such a s searching
objects with a given attribute value, specifying the attributes of a n
object, clustering objects based on similarity, counting objects, and
visual object comparison. Accuracy of operation results and time to completion are two commonly used measures. Taking such a n approach
would make i t easier to design a n evaluation study and to attribute the
task performance to differences in visualization designs. However,
because of the complexity of real-life system interface tasks, the validity
of such a design and the applicability of research conclusions are sometimes questioned by practitioners. For example, several studies have
been conducted to evaluate popular tree representations such as
Hyperbolic Tree (Pirolli, Card, & Van Der Wege, SOOO), Treemap (Stasko
e t al., ZOOO), multilevel SOM (Ong et al., in press), and Microsoft
Windows Explorer. These studies all involve simple visual operations of
node searching and node comparison. Representations such as Treemap
168 Annual Review of Information Science and Technology
Figure 4 . 1 2 ~
Expert Indicator. The interface is divided into subgardens
based on the SOM output. Each subgarden is a subtopic. Each
flower represents one person, where the number of petals
represents the number of messages posted by this person for
this subtopic the number of leaves represents the number of
threads participated by this person in the subtopic and the
height of flowers represents how long this person has stayed
in this subtopic. Figure available in color at http://www.
asis.org/Publications/ARIST/vol39ZhuFigures.html
are multilevel SOM effective for node-comparison operations because
they offer more visual cues for each node, while hyperbolic tree and
Microsoft Windows Explorer, providing a global picture, are more effective in supporting node-searching operations. But how these basic nodesearching and node-comparison operations are related to a user’s
real-life, complex searching or browsing tasks is unclear.
Complex, realistic, task-driven evaluation studies have been conducted frequently in visualization research, for example, Pohl and
Purgathofer (2000); Risden, Czerwinski, Munsner, and Cook, (2000);and
North and Shneiderman (2000). The experimental tasks are based on
functionalities that the visualization system aims to provide. Subjects
conduct tasks such a s maintaining a hierarchy of subject categories
Information Visualization 169
(Risden et al., ZOOO), writing a paper (Pohl & Purgathofer, 20001, or
selecting appropriate visualization methods to display different information (North & Shneiderman, 2000). The usefulness of a given visualization system can be directly measured by this approach, but it is difficult
to identify or isolate the visualization factors that contribute to user performance (partially due to the intertwining nature of the system, task,
and user).
Although laboratory experimentation has been useful in information
visualization research, we believe other well-grounded behavioral methods such as protocol analysis (to identify qualitative observations and
comments), individual and focus group interviews (to solicit general
feedback and group responses), ethnographic studies (to record behaviors and organizational cultures), and technology and system acceptance
surveys (to understand group or organizational adoption process) also
need to be considered. Instead of relying on a one-time, quantitative laboratory experiment, visualization researchers can triangulate and substantiate their findings using qualitative, long-term assessment
methodologies.
Fundamental Perception Studies and Theory Building
Unlike empirical usability studies, fundamental perception studies
are grounded in psychology and neuroscience. Theories from those disciplines are used to understand the perceptual impact of such visualization parameters as animation (Bederson & Boltman, 1999), information
density (Pirolli et al., ZOOO), 3-D effect (Tavanti & Lind 20011, and combinations of visual cues (Nowell, Schulman, & Hix, 2002). What distinguishes this type of study from conventional perception studies is that it
usually involves some form of computer-based visualization. For
instance, Bederson and Boltman (1999) used the Pad++ program to
study the impact of animation on users' learning of hierarchical relationships; a hyperbolic tree with fish-eye view was applied by Pirolli et
al. (2000) to study the effect of information density. Hypotheses, tasks,
and measures are developed under the guidance of theories from psychology. However, because of the unique system, task, and perception
factor combinations, results may be applied only to the particular visualization system under study. A well-grounded visualization theory and
research framework that can be used to guide visualization system
development is urgently needed.
Summary and Future Directions
This chapter has reviewed information visualization research based
on a framework of information representation, user-interface interaction, and information analysis. We have presented the field's history, theoretical foundations, and three important, emerging application domains:
digital libraries, the Web, and virtual communities. We summarized the
170 Annual Review of Information Science and Technology
status of visualization system evaluation research and suggested areas
for future research, in particular, long-term, qualitative, theorygrounded evaluation studies.
Although this chapter focuses on the visualization of textual information, many associated techniques can be applied to multimedia visualization. For example, the visualization system described in Christel,
Cubilo, Gunaratne, Jerome, 0, and Solanki (2002) applied the video
indexing and segmentation techniques developed a t the Carnegie
Mellon University Informedia project (Wactlar, Christel, Gong, &
Hauptmann, 1999) to help users browse video digital libraries.
In summary, information visualization can help people gain insights
from large-scale collections of unstructured information. Developments
in computer hardware and software will not only advance information
visualization technology but also stimulate wider adoption. Even though
more-and more innovative-visualization systems are expected to be
developed soon, there is also a critical need for advancing visualization
theories and evaluation.
Lastly, we suggest several promising research areas that could benefit
from information visualization research: visual data mining, virtual reality-based visualization, and visualization for knowledge management.
Visual Data Mining
Visual data mining enables users t o identify patterns that a data mining algorithm might find difficult to locate. Visualization could play two
types of roles in a data mining tool. It could support interaction between
users and data-the exploration of an unknown data set. Integrated
with such user-interface interaction approaches as zooming or fish-eye
view, representation methods such as scatter plot, parallel coordinates,
glyphs, and self-organizing maps can be applied to project data (Simoff,
2002). Visualization can also support interaction with the analytical
process and output of a data mining system. Such interaction can incorporate human expertise and judgment (Hinneburg, Keim, & Wawryniuk, 1999; Niwa, Fujikawa, Tanaka, & Oyama, 2001; Wong, 19991,
which may be critical to the performance of a system but impossible to
incorporate in computer code. How to integrate data mining algorithms
and various visualization techniques seamlessly in an effective analytical process is still a pressing research challenge.
Virtual Reality- Based (Immersive) Visualization
Although most visualization tools rely on human visual perception to
deliver patterns, virtual reality (or immersive) technology tries to take
advantage of the entire range of human perceptions, including auditory
and tactile sensations. However, in addition to the technological challenges such as inpuVoutput devices, virtual reality research still faces
many human factors challenges, such as individual differences,
inpuYsensor overload, and cyber-sickness (Kalawsky, 1993; Stanney,
Information Visualization 171
1995). In spite of such challenges, we believe that the current generation,
which has grown up with Internet surfing and video games, will be more
ready to adopt future virtual reality-based visualization technologies.
Visualization for Knowledge Management
Visualization can support knowledge management by facilitating
knowledge sharing and knowledge creation. Knowledge itself is difficult
to visualize because it often exists only in someone’s mind (referred to as
tacit knowledge) (Nonaka, 1994). Visualization can accelerate internalization by presenting information in an appropriate format or structure
or by helping users find, relate, and consolidate information (and thus
helping to form knowledge) (C. Chen & Paul, 2001; Cohen, Maglio, &
Barrett, 1998; Foner, 1997; Vivacqua, 1999).As knowledge management,
data mining, and knowledge discovery research advances, we may begin
to move from “information visualization” to “knowledge visualization.”
Endnote
1. The figures in this chapter are available in color at http://www.asis.org/Publications/
ARIST/vol39ZhuFigure~.html
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