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Hierarchical Data Visualization for Atomic
Plant Data∗
Takayuki ITOH∗∗ , Shiho FURUYA∗∗ , Hiroyuki OHSHIMA∗∗∗ andKoji OKAMOTO†∗∗ Graduate School of Humanities and Sciences, Ochanomizu University

2–1–1 Otsuka, Bunkyo-ku, Tokyo 112–8610, Japan
E-mail: {itot, shiho}@itolab.is.ocha.ac.jp∗∗∗ Computational Fast Reactor Engineering Group, Japan Atomic Energy Agency

4002 Narita, O-arai, Ibaraki 311–1393, Japan
E-mail: ohshima.hiroyuki@jaea.go.jp† Human and Engineered Environmental Studies, The University of Tokyo

5–1–5 Kashiwanoha, Kashiwa-shi, Chiba 277–8563, Japan
E-mail: okamoto@k.u-tokyo.ac.jp

Abstract
This paper introduces our own hierarchical data visualization technique, HeiankyoView, and discusses its potential for fluid science. HeiankyoView represents hierarchy as nested rectangles, and displays thousands of data elements in one display
space. The paper briefly introduces the algorithm and various applications of HeiankyoView, and discusses our attempts for visualization of computational fluid dynamics
data. Finally, the paper introduces our work on visualization of atomic plant data using
HeiankyoView.
Key words : Hierarchical data visualization, HeiankyoView, Computational fluid dynamics, Atomic plants.

1. Introduction∗

Received 14 Dec., 2007 (No. XX-XXXX)[DOI: 10.1299/XXX.3.1]

Many visualization techniques are categorized into two fields: scientific visualizationand information visualization. Most of scientific visualization techniques represent various
phenomena in physical spaces, which are usually obtained by real measurements or computational simulations. On the other hand, information visualization techniques represent general
information, including non-scientific, non-geometric and non-spatial problems. These two
fields have an independent history of research. However, some recent scientific visualization
works have integrated information visualization techniques.
Shneiderman(1) reported that data structures for information visualization can be categorized into the following seven types: one-dimensional, two-dimensional, three-dimensional,
more than three dimensional, tree, graph, and time-varying data. We have been interested invisualization of tree data (”hierarchical data” in this paper) for several years. Information indaily life is often hierarchically organized; examples include structures of human organizations and file systems of computers.
There have been many research projects on hierarchical data visualization. Many of theseprojects represent tree structures as a set of nodes and edges connecting parent and/or childnodes, where the concept is often called the ”node-link approach”. Many of these approaches
provide user interfaces to explore the hierarchy interactively; Hyperbolic Tree(2) is one of themost famous works. Many of other works spread data items of the lowest hierarchy of thedata, where the concept is often called a ”space-filling approach”; TreeMaps(3) is one of themost famous works. We have also presented a space-filling hierarchical data visualization
technique(4)(5) . The following sections briefly introduce our hierarchical data visualization
technique and its applications.
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2. Hierarchical Data Visualization Technique ”HeiankyoView”
Figure 1(Left) shows an example of a tree, and Figure 1(Right) shows the representationof the same data by HeiankyoView, which represents leaf-nodes as painted square icons, andbranch-nodes as nested rectangular borders.

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(Left) Node-Link representation of a tree. (Right) Representation of the samedata by HeiankyoView.

The processing flow of HeiankyoView is as follows. HeiankyoView first packs icons
that represent the lowest-level leaf nodes (e.g., 1 to 4 in Figure 1) under a branch node (e.g.,
A in Figure 1), and then encloses the icons by rectangular borders, which correspond to thebranch node. HeiankyoView packs a set of rectangular borders that belong to higher levelsof the hierarchy, and generates the larger enclosing rectangular borders that correspond to thehigher branch nodes (e.g., D in Figure 1). Repeating the process from the lowest level ofthe hierarchy toward the highest level, HeiankyoView places all of the data items onto thelayout area. Here, the packing algorithm for icons and rectangles is the key technology for
HeiankyoView. We presented a triangular-mesh-based rectangle packing algorithm(6) , andthen applied an improved rectangle packing algorithm, as described in(4)(5) . Both algorithms
place icons and rectangles one-by-one onto display spaces, while the algorithms choose theirpositions from multiple candidate positions.
HeiankyoView places thousands of leaf-nodes into one display space while satisfying thefollowing conditions:• It never overlaps the leaf-nodes and branch-nodes in a single hierarchy of other nodes.
• It attempts to minimize the display area requirement.
• It draws all leaf-nodes with equally shaped and sized icons.
This representation style is suitable for visualizing thousands of leaf-nodes of hierarchical data in one display space, rather than representing hierarchical relationships between
parent and children nodes. All of the leaf-nodes are displayed as equally-sized clickable icons.
The naming of HeiankyoView came from ”Heiankyo”, an ancient palace in Kyoto-city.
Since all blocks in Heiankyo orthogonally align, and therefore the visualization results of
HeiankyoView often look somewhat similar to the map of Heiankyo. That is a reason why we
named this technique HeiankyoView.

3. Extensions and Applications of HeiankyoView
We have applied HeiankyoView to a variety of hierarchical data.
Our early works focused on visualization of statistics of computational events recorded invarious log files. We first visualized the statistics of Web accesses, which forms a hierarchy of
Web pages according to their directory structures, and represents the numbers of accesses ofeach Web page by colors or heights of icons. We also visualized network intrusion detection
results(4) , which forms a hierarchy of computers according to their IP addresses, and represents the number of malicious accesses of each computer by the heights of icons. These works
aimed to provide all-in-one display of statistics of accesses, allowing users to discover correlations between hierarchy structures and the accesses. As a result, we discovered concentrated
accesses to specific organizations, based on the visualization results.
Recent our works focus on the visualization of clustered data. We visualized the experimental values of drugs, which are clustered according to their chemical structures(5) . We also
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visualized the clustering results of questionnaires, newspaper articles, gene expression data,and partial geometric features of protein molecular surfaces.
Figure 2 shows an example of visualization of newspaper articles, which displays two
HeiankyoView visualizations from side to side. Here, the left side represents clustering resultof keywords, and the right side represents clustering result of documents. We call this technique ”Sakyo and Ukyo”, where ”Sakyo” is the east side of Heiankyo, and ”Ukyo” is the westside of Heiankyo. HeiankyoView provides the user interface so that the user can interact withthe both the left and right sides at the same time. When the user clicks icons corresponding to
keywords in the left side, the importance of the keywords in the documents is indicated by thecolors in the right side. Similarly when clicking icons corresponding to documents in the rightside, the body of the document is indicated in another window, and sum of the importance forthe cluster of clicked documents is indicated by colors in the left side.
Figure 3 shows an example of visualization of hierarchical drug data(7) . Here, five colors represent susceptibility to five isozymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6, andCYP3A4) for each drug. We call this technique ”JunihitoeView”, where ”Junihitoe” is acolorful costume in Heian Period. This technique is suitable for the visualization of multidimensional (5-dimensional in this example) hierarchical data.
Clicking keywords, its importance is indicated by colors in the right side

Keywords

Documents

Height: sum of importance
Clicking documents, body of the document is indicated, and sum of importancefor the cluster of clicked document is indicated by colors in the left side

Fig. 2

Fig. 3”Sakyo and Ukyo” for visualization of corpus data.

”JunihitoeView” for visualization of drug data.

We are also using HeiankyoView as a content browser. As shown in Figure 4, we presented ”CAT” (Clustered Album Thumbnails), a browser of large-scale image collections(8) ,which maps many images onto icons. We also presented a similar work for visualization of3

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music collections, which displays automatically selected music icon images.

Fig. 4”CAT” for visualization of an image collection.

In summary, most of the presented applications of HeiankyoView deal with non-geometricand non-spatial information.

4. Visualization of Fluid Dynamics Simulation Results by HeiankyoView
This section considers the visualization of fluid dynamics simulation results in HeiankyoView. Figure 5(Left) shows an example of a visualization of a weather simulation result. This
example is from volume data consisting of orthogonally aligned grid points and rectangularelements. Each grid-point has two scalar values (pressure and temperature) and a vector value(air flow). The figure represents distribution of the pressure by three colors of isosurfaces, andthe air flow by several streamlines.
Figure 5(Right) shows an example of visualization of the same volume data by HeiankyoView. To obtain the example, we first extracted 16 slice planes from the volume along thex-axis, and selected 9x9 grid points from the each of the slice planes. We visualized the data
as 16 groups of 81 bars, by calculating their colors according to the pressure, and their heights
according to the temperature.
Here, both examples denote high pressure by red, and low pressure by blue or green. We
can recognize that the pressure varies mainly along the z-axis, not the x- or y-axis, from theleft example. We can also recognize that the pressure does not vary so much along x-axis fromthe right example compared with the left example. Meanwhile, we can easily recognize thecoherency between pressure and temperature from the right figure. Briefly, temperature getshigher when pressure is lower.

z

y

x

Fig. 5(Left) Visualization of volume data by isosurfaces and streamlines. (Right)
Visualization of volume data by HeiankyoView.

We think that most of researchers and engineers working with fluid dynamics simulation
used to rely on volume visualization as shown in Figure 5(Left) rather than Figure 5(Right).
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One reason for this preference may be that HeiankyoView is not very suitable to visualize thespatial coherency of scalar fields. To solve the problem we may need to provide the linkage
to physical space visualization techniques. Providing the user interface to interact the left andright visualization results in Figure 5, as Sakyo and Ukyo provides as shown in Figure 2, will
be more comprehensive.
On the other hand, we think that information visualization techniques may provide different useful aspects to visualize volume data. Here we discuss the features of visualizationof fluid dynamics data by HeiankyoView.
Simultaneous representation of multi-dimensional values is a feature of information visualization techniques. Most volume visualization techniques represent distribution of scalarvalues by colors; particularly in Figure 5(Left), colors of isosurfaces denote the scalar values. Here, HeiankyoView can be better in visualizing multi-dimensional values rather thansuch isosurface representations. Figure 5(Right) shows an example in which HeiankyoView
represents two scalar fields simultaneously, by colors and heights of the bars. The representation is suitable to understand the distribution and correlation of the two scalar fields. We
experimentally determine to assign scalar fields as follows:• We assign a scalar field to heights, if larger values are more important or caution
needed.
• Otherwise, we assign the scalar field to colors.
If we do not have any scalar fields suitable for heights, or if we would like to simultaneously
visualize more than two scalar fields, JunihitoeView will be more eﬀective.
Another feature is a discovery of very local but important phenomena. Indirect volume
visualization techniques (e.g., isosurface, streamline) represent partial features of scalar orvector fields; therefore, indirect visualization may miss important portions of the fields. Direct volume visualization techniques (e.g., volume rendering) represent the whole distributionof scalar or vector fields by using semi-transparent rendering techniques. It is a good approach to briefly look the overall distribution of the fields; however, if transfer functions orother configurations are not finely adjusted, small features will not be noticeable. Automatictransfer function setting has been an area of active research regarding solving this problem(9) .
HeiankyoView can be an alternative approach to represent very local but important featuresof scalar fields.

5. Visualization of Measurements and Simulations of Atomic Plants
This section introduces our visualization work of measurements and simulations of atomic
plants, applying our hierarchical and time-varying data visualization techniques.
5.1. Visualization of Atomic Plant Data by HeiankyoView
We visualized actual measurement data from Monju, a prototype fast breeder reactor built
in Fukui prefecture. Monju has a measurement recording system, called MIDAS, which stores
512 independent measurements, including temperatures, pressures, velocities, voltages, and soon. We constructed a hierarchy of the 512 points by two-level division. First we divided themaccording to their positions, and then according to their units (e.g., temperature, pressure, andvelocity). Then, we visualized the hierarchical data by HeiankyoView, where spatially closer
and identical unitary measurement values become concentrated in one rectangular area. We
represented the measurement values by the colors and heights of the bars.
Figures 6 and 7 denote transfer functions to calculate the colors and heights of the bars.
Our implementation assumes that lower and upper limits of steady state are set for each measurement. If the measured value is within the range of lower and upper limits, HeiankyoView
draws the bar corresponding to the measured value as gray, because the value does not require
the user’s attention. If the measured value is higher than the upper limit, HeiankyoView draws
the bar as red or yellow. If the measured value is lower than the lower limit, HeiankyoView
draws as blue or green. Our implementation also assumes that diﬀerence of measured values
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Fig. 6

Color transfer function.

Fig. 7Height transfer function.

between current and previous time can always be calculated. If the absolute value of the difference is large, HeiankyoView draws a tall bar corresponding to the measured value, because
the value requires the user’s attention. Otherwise, HeiankyoView draws the bar as short.
Figure 8 shows a visualization result, where a bar in a pink circle denotes that the measured value was suddenly increasing. At that time Monju had an anomaly: liquid natrium
leaked from a pipe of a cooling system, and therefore the temperature readings suddenly
jumped very high. The sudden change was observed at only one point, but this change certainly warranted operator attention. HeiankyoView can place emphasis on the visualizationof important points, distinguishing them graphically from the large number of uninteresting
points.
Figure 9 shows another visualization result, where many blue short bars denote that there
were many measured values that were extremely low but staying flat. Several minutes before
that time, Monju had a trip operation to stop the system, and then many instruments reported
extremely high or low values. The blue short bars suggest that the trip operation proceededsafely, because the measured values were extremely low but stable. Such bars intensively appear in several specific groups, as shown in pink circles in Figure 9. These graphical elements
denote that the trip operation works normally. Physical values in the trip operation go down
block-by-block, and this phenomenon is well-shown in the visualization result.
In addition to the visualization of real measurement data shown in Figures 8 and 9, we
are working with simulated data of atomic plants. We think a similar approach can be applied
to various fluid science problems as well as to measurements and simulations of atomic plantdata.
5.2. Visualization of Time-varying Atomic Plant Data
We assume that measurement or simulation data of atomic plants is collectible into tables, where columns denote measurement points, and rows denote the time. Here, examples

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Fig. 8 Visualization result (1). A bar in a pink circle denotes that the measured value
is suddenly increasing.

of visualization of atomic plant data in Figures 8 and 9 represent sets of hierarchical measurement values at specific times. On the other hand, we think that many users are interested in
the visualization of time variation of the measurement values, in addition to the hierarchicalvisualization at a specific time. Let us discuss the simultaneous visualization of time-varying
and hierarchical data in one display.
Figure 10 shows a snapshot of the presented technique, which displays two windows. The
left thin window represents time-varying data, and the right window represents hierarchicaldata. The left window contains many buttons that correspond to specific times. When a userpresses one of the buttons, the right window represents the hierarchical measurement values
at the specified time.
Here we assume that the width of the black drawing area of the thin window is approximately 150 or 200 pixels. It is not always easy to visualize time-varying data of the hundreds
or thousands of measurement values in such a narrow drawing area. To solve the problem, weapplied a concept of a quantization table, which is often used in compression of sound data,
for the visualization of large-scale time-varying data in the narrow drawing area.
Sound compression techniques look up the quantization table to multiply the coeﬃcientsto each frequency elements, so that important frequency signals are finely coded, and otherfrequency signals are roughly coded. Our technique is inspired by the above mechanism:
the technique finely represents remarkable measurement values, such as the values that areconsiderably higher or lower than usual values, or the values which are drastically increasingor decreasing. The technique realizes importance-based representation of the time-varyingdata.
Let n be the number of measurement values, and m be the horizontal resolution of thedrawing area. Also, take fi (t) as i-th measurement value at the time t, where fi (t) is normalized
within the range [0, 1]. The technique first calculates the importance of the i-th measurement
value pi . Here, calculation of importance is application oriented, but in the particular case of
atomic plant data we calculate pi by the following equation:pi = S 1 | fi (t) − 0.5| + S 2 | fi (t)|(1)

where S 1 and S 2 are positive constant values.
The technique then calculates the width of the i-th measurement value, wi , by the follow-

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Fig. 9Visualization result (2). Many blue short bars denote that there are manymeasured values that are extremely low but staying flat.

ing equation:
wi = m  npi

i=1 pi(2)

After calculating the widths of all measurement values by the above equation, the technique represents the distribution of the measurement values at t. The technique sorts themeasurement values according to fi (t), and calculates colors of the measurement values. Finally, it paints rectangular regions corresponding to the measurement values, in the sorted
order, applying the calculated colors and the widths wi .
Figure 11 shows examples of the time-varying data. A circle drawn in Figure 11(Left)denotes the time shown in Figure 8. At the time only one measurement value drasticallyincreased, and it is emphatically represented in Figure 11(Left). A circle drawn in Figure11(Right) denotes the time shown in Figure 9. At the time many measurement values were
extremely low, and they are certainly represented in Figure 11(Right).
We call our time-varying data visualization technique ”KonjakuMonogatari”, comingfrom the name of a novel in Heian Period which reflects the old stories.
One possible problem with KonjakuMonogatari is that it may cause time-space aliasing.
If a user randomly chooses times by pressing the buttons, HeiankyoView will suddenly updates the display of values. It may be diﬃcult to recognize the changes of values properly.
We would like to implement a morphing technique between two specified times, so that thetechnique can smoothly represent the changes of values and make it easier to recognize them.
We had a user experience of the presented technique with 11 examinees. In this experience we asked the examinees to check caution needed times by visualizing the results of sevenatomic plant simulations. We also asked a specialist of atomic plant simulation to similarly
check caution needed times by visualizing the same data. The result was successful, since thenumber of examinees who checked the times reported by the specialist was 9.5 in average.
There have been several related works in the visualization of time-varying values using
thin drawing areas. ThemeRiver(10) represents time-varying frequency values of keywords inlarge scale documents, as a metaphor of a river in thin drawing areas. It looks very similar
to our visualization results, but it did not focus on visualization of distribution of hundreds
or thousands of time-varying values. T-Map(11) represents the change of topological featuresof scalar fields in thin drawing areas. T-Map is also diﬀerent from our technique, since it
did not focus on visualization of distribution of hundreds or thousands of time-varying values
themselves.
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Button for specifying time

Fig. 10

Fig. 11

Pressing a button, HeiankyoView represents
measurement values at the specified time.

Visualization of time-varying data on a thin window.

Visualization result (3). (Left) Time-varying data around the specific timeshown in Figure 8. (Right) Time-varying data around the specific time shownin Figure 9.

6. Conclusion
This paper first introduced our hierarchical data visualization technique HeiankyoView
and its applications. The paper then discussed the visualization of fluid dynamics simulation
results by HeiankyoView, and introduced our work on the visualization of measurements andsimulations of atomic plants.
We think that information visualization techniques (including HeiankyoView) have potential applications to fluid science, and would like to demonstrate this potential in our following our future work:• Experiments visualizing various fluid dynamics simulation data using HeiankyoView,
in addition to the visualization of simulation data of atomic plants.
• Development of information visualization techniques for other types of data structures(e.g., time-varying or multi-dimensional data), for visualizing fluid dynamics simulation data.
• Integration of volume visualization techniques and HeiankyoView (or other information visualization techniques), for more intelligent analytics and discovery.

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Acknowledgements
Ms. Fumi Yasuda and Prof. Tetsuya Kawamura, with Ochanomizu University, provided
volume data of weather simulation. Mr. Takaaki Sakai and Mr. Takashi Iwasaki, with Japan
Atomic Energy Agency, supported the arrangement of measurement and simulation data of
atomic plants.
The present study includes the results of ”Large-scale information visualization for management of atomic systems” entrusted to ”Ochanomizu University” by the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT).

References( 1 ) Shneiderman, B., The Eyes Have It: A Task by Data Type Taxonomy for Information
Visualization, Proceedings of IEEE Symposium on Visual Languages ’96 (1996), 336343.
( 2 ) Lamping, J., Rao, R., The Hyperbolic Browser: A Focus+context Technique for Visualizing Large Hierarchies, Journal of Visual Languages and Computing, Vol. 7, No. 1(1996), 33-55.
( 3 ) Johnson, B., Shneiderman, B., Tree-Maps: A Space Filling Approach to the Visualization of Hierarchical Information Space, Proceedings of IEEE Visualization ’91 (1991),
275-282.
( 4 ) Itoh, T., Takakura, H., Sawada, A., Koyamada, K., Hierarchical Visualization of Network Intrusion Detection Data in the IP Address Space, IEEE Computer Graphics andApplications, Vol. 26, No. 2 (2006), 40-47.
( 5 ) Itoh, T., Yamashita, F., Visualization of Multi-dimensional Data of Bioactive Chemicals
Using a Hierarchical Data Visualization Technique ”HeiankyoView”, Proceedings ofAsia Pacific Symposium on Information Visualization (APVIS) 2006 (2006), 23-29.
( 6 ) Itoh, T., Yamaguchi, Y., Ikehata, Y., Kajinaga, Y., Hierarchical Data Visualization Usinga Fast Rectangle-Packing Algorithm, IEEE Transactions on Visualization and Computer
Graphics, Vol. 10, No. 3 (2004), 302-313.
( 7 ) Yamazawa, M., Itoh, T., Yamashita, F., Visualization and Level-of-Detail Control for
Multi-Dimensional Bioactive Chemical Data, Proceedings of 12th International Conference on Information Visualization (IV08) (2008).
( 8 ) Gomi, A., Miyazaki, R., Itoh, T., Li, J., CAT: A Hierarchical Image Browser Using a
Rectangle Packing Technique, Proceedings of 12th International Conference on Information Visualization (IV08) (2008).
( 9 ) Takahashi, S., Takeshima, Y., Fujishiro, I., Topological Volume Skeletonization and Its
Application to Transfer Function Design, Graphical Models, Vol. 66, No. 1 (2004), 2449.
(10) Havre, S., Hetzler, B., Whitney, P., Nowell, L., ThemeRiver: Visualizing Thematic
Changes in Large Document Collections, IEEE Transactions on Visualization and Computer Graphics, Vol. 8, No. 1 (2002), 9-20.
(11) Fujishiro, I., Otsuka, R., Takahashi, S., Takeshima, Y., T-Map: A Topological Approachto Visual Exploration of Time-Varying Volume Data, Springer Lecture Notes in Computer Science, Vol. 4759 (2008), 176-190.

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