Journal Visualization by a Dual Hierarchical Data Visualization Technique1

Kana Shiratori∗

Takayuki Itoh†

Ochanomizu University

Ochanomizu UniversityI NTRODUCTION

Researchers often search for specific papers from journals, or lookover the trends of the journals. This task is not always easy: for example, various kinds of papers may be extracted if we use generalterms for query operations, and it may prevent to discover the interesting papers. We think journal visualization is useful to look thedistribution of queried papers or overview of the journal itself. This
poster presents a journal visualization technique, applying our dual
hierarchical data visualization technique [2]. The technique firstly
extracts meaningful keywords from a journal, and calculates importance of the keywords for each paper or author. It finally represents
the distribution of keywords, papers, and authors by our own dual
hierarchical data visualization technique. The poster also provides
our experiments that demonstrate the effectiveness of the presentedtechnique.
2R ELATED W ORK

Many of document visualization techniques represent clusters orrelationships of documents constructed based on the co-occurrenceof keywords [1]. Our technique in this poster is also based on theco-occurrence of keywords. DualNAVI [4] is one of the existing
techniques most related to our work, which represents relationshipof keywords in the left side of a window, and a list of documents in
the right side of the window.
We proposed a hierarchical data visualization technique [3],which represents leaf-nodes of hierarchical data as colored icons,
and branch-nodes as nested rectangular regions. Our dual visualization technique [2] applies this hierarchical data visualization fromside to side, to effectively represent matrix data. The technique
firstly applies a clustering algorithm to columns and rows of matrix data independently, and generates two hierarchical data as theclustering results. The technique then displays the two hierarchical data respectively, while it provides a user interface so that users
can interactively explore the two hierarchical data each other. We
have applied this technique to visualization of newspaper corpus
data that represents the relationship among keywords and articlesof the newspaper. In addition to the representation of keywords anddocuments, the technique presented in this poster also focuses on
representation of authors.
3J OURNAL V ISUALIZATION

Figures 1 and 2 show snapshots of journal visualization by the presented technique. The technique visualizes hierarchically clusteredauthors and papers by our dual hierarchical data visualization technique, and displays keyword buttons in the left end of the display
space.
3.1 Extension of our dual hierarchical data visualization
Our previous work [2] visualized newspaper corpus data, by constructing a 2D keyword-article matrix and filling the importance∗ e-mail: kana@itolab.is.ocha.ac.jp† e-mail:itot@is.ocha.ac.jp

Keywords

Authors

PapersAlgorithmic works

Keyword “CG”

Two active authors

Application systems

Figure 1: Snapshot of the presented technique (1). A keyword ”CG”
is selected.
Keywords

Authors

PapersKeyword “Music”

Music related authors

Music related papers

Figure 2: Snapshot of the presented technique (2). A keyword ”Music” is selected.

values in. The technique presented in this poster extends the mechanism by adding author information. It constructs a 3D keywordpaper-author matrix filling the importance values in, and then visualizes the 3D matrix.
3.2 Processing flow of journal visualization
Figure 3 shows the data structure and processing flow we supposein this poster. Here, we describe the keyword-paper matrix as T1 ,and the keyword-author matrix as T2 . We also describe papers as r1to rn (n is the total number of papers), authors as s1 to sl (l is thetotal number of authors), and keywords as c1 to cm (m is the total
number of keywords). In the matrix data, ai j denotes an importanceof the j-th keyword in the i-th paper, and bk j denotes an importanceof the j-th keyword of the k-th author.
The technique firstly applies a clustering algorithm to papers andauthors independently. It then displays hierarchical clusters of papers in the right side of the window, and hierarchical clusters ofauthors in the left side of the window. It also displays the keywords
as a vertically aligned set of buttons. Colors of icons and buttons
denote the importance values. The importance is high if the color

is close to red. On the other hand, it is low if the color is close toblue.
3.3 Interaction among keywords, authors, and papers
The presented technique has a mechanism to click keywords, papers, and authors, so that users can interactively explore the matrix
data. For example, when a user clicks a keyword button, icons ofpapers and authors related to the clicked keyword are highlighted.
Similarly, when a user clicks an icon of the paper/author, icons
of authors/papers and keyword button related to the clicked paper/author are highlighted.
Keyword operation
Suppose that a user clicks a keyword button c j . The technique scansthe values a1 j to an j in the matrix T1 , and uses the scanned values
ai j to calculate colors of icons of papers ri . Similarly, the techniquescans the values b1 j to bl j in the matrix T2 , and uses the scannedvalues bk j to calculate colors of icons of authors sk .
Reflection of author/paper operation to keywords
Suppose that a user clicks an icon of a paper ri . The technique scansthe values ai1 to aim in the matrix T1 , and uses the scanned valuesai j to calculate colors of keyword buttons c j . Red lines and circles
in Figure 3 denote this processing flow. The technique applies thesimilar mechanism when a user clicks an icon of an author sk .
Reflection of author/paper operation to papers/authors
Suppose that a user clicks an icon of a paper ri . The technique calculates the color of the icon of the k-th author sk from the value∑mj=1 ai j bk j , which corresponds to the sum of importance of all
keywords of the k-th author bk j weighted by the importance of theclicked i-th paper ai j . Blue lines and circles in Figure 3 denote thisprocessing flow. The technique applies the similar mechanism tocalculate colors of icons of papers, when a user clicks an icon of anauthor sk .

T2

T1
c1

cj

cm

r1

ri

c1

cm

cj

s1
aij

rnsk

bkjsl

∑ abmj =1

E XPERIMENT

We applied a new journal ”Journal of the art and science”1 published in Japan since 2002 as test data of the presented technique.
In this experiment, we firstly extracted abstract and author information from the all 134 papers of the journal. At this moment 283
authors have contributed to the journal. We then selected important keywords from the abstracts, by using Japanese language morphological analysis software ”Chasen”2 , and word importance calculation software ”termex”3 . In our experiment we automaticallyselected top 40 keywords, and then manually selected especiallymeaningful 23 keywords from the 40 keywords.
1

http://art-science.org/journal/index.html

2 http://chasen.naist.jp/hiki/Chasen/
3 http://gensen.dl.itc.u-tokyo.ac.jp/

5 C ONCLUSION
The poster presented a journal visualization technique applying ourdual hierarchical data visualization technique. We would like tohave experiments with larger journal data as a future work.
Also, we would like to extend the functionality of the technique.
For example, we would like to represent visibility of authors andpapers as 3D bars so that users can easily focus on popular authorsor papers.
This work has been partially supported by Japan Society of thePromotion of Science under Grant-in-Aid fir Scientific Research(C) No. 18500074.
R EFERENCES

ij kj

Figure 3: Processing flow of color calculation with click operations.

4Figure 1 shows a visualization result when a user selects a keyword ”CG”. The left part of the visualization result displays especially bright (red and orange) two icons in a central cluster. Itdenotes that the two authors especially contributed to the journal
by submitting CG-related papers. The right part of the visualization result shows that there are two clusters (marked by pink andred circles) that contain especially bright icons. The upper cluster
marked by pink contains papers highly related to other several keywords, ”modeling”, ”3D”, and ”image”. The lower cluster marked
by red contains papers highly related to other several keywords,”support system” and ”design”. The result denotes that the presented technique successfully divided CG-related papers into twoclusters of algorithmic papers and application system papers, andthe visualization technique successfully represented the two clusters. We think that such representation helps users to focus on interested clusters of the papers.
Figure 2 shows a visualization result when a user selects a keyword ”Music”. The left part of the visualization result shows thatmost of music-related authors construct a single cluster. On theother hand, several other music-related authors are out of the cluster; they are in the clusters which consist of authors of image- orvisualization-related papers. The right part of the visualization result also shows that most of music-related papers construct a singlecluster; however, several other papers are out of the cluster. Actually, most of music-related papers in the journal are related eachother, while the papers present music technologies such as composition assistance system and sound editing algorithm. On the otherhand, several papers are also related to other keywords; actually oneof the papers presents an algorithm for matching between images
and tunes. The visualization result denotes that users can discoveroutlier papers related to specific keywords (”Music” in this case) by
the presented technique.

[1] K. Andrews, W. Kienreich, V. Sabol, J. Becker, G. Droschl, F. Kappe,
M. Granitzer, P. Auer, K. Tochtermann, The InfoSky Visual Explorer:
Exploiting Hierarchical Structure and Document Similarities, Information Visualization, Vol. 1, No. 3, pp. 166-181, 2002.
[2] T. Itoh, H. Tachibana, Visualization of Corpus Data by a Dual Hierarchical Data Visualization Technique, The 7th NTCIR (NII Test Collection for Information Retrieval Systems) Workshop Meeting on Evaluation of Information Access Technologies, 534-541, 2008.
[3] T. Itoh, H. Takakura, A. Sawada, K. Koyamada, Hierarchical Visualization of Network Intrusion Detection Data in the IP Address Space,IEEE Computer Graphics and Applications, 26(2), 40-47, 2006.
[4] A. Takano, Y. Niwa, S. Nishioka, M. Iwayama, T. Hisamitsu,O. Imaichi, H. Sakurai, Associative Information Access Using
DualNAVI, Kyoto International Conference on Digital Libraries(ICDL’00), pp.285-289, 2000.