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Title

A scalable "exploranation" technique for hierarchically indexed tabledata

Authors

Natsuki Hosokawa, Kohei Arimoto, Ken Wakita

Citation

VINCI '20: Proceedings of the 13th International Symposium on Visual
Information Communication and Interaction, , No. 24, pp. 1-5

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2020, 12Note(C) ACM 2020. This is the author's version of the work. It is posted here
for your personal use. Not for redistribution. The definitive Version ofRecord was published in VINCI '20: Proceedings of the 13th
International Symposium on Visual Information Communication andInteraction, http://dx.doi.org/10.1145/3430036.3430069.

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A Scalable “Exploranation” Technique for Hierarchically Indexed
Table Data
Natsuki Hosokawa

Kohei Arimoto

Ken Wakita

hosokawa.n.ac@m.titech.ac.jp
School of Computing, Tokyo Instituteof Technology

khyarmt@gmail.com
Teikoku Databank and The Universityof Tokyowakita@is.titech.ac.jp
Tokyo Institute of Technology

B Map PaneE

Explanation PaneF Navigation Tools

A Region Selection Pane
D Data Visualization PaneC Attribute Selection Pane

Figure 1: SPIViewer is an “exploranative” VA tool for comparing UN member states using the Social Progress Index.[15]ABSTRACT

1

Data analytics tools that combine automated text generation and visualization techniques suffer from scalability problems. The amountof the generated text explodes with the increase of items and attributes. This study addresses this problem for table data whose
attributes and data items are hierarchically organized. In our approach, the user’s point of view is modeled by the dual focalization
axis, which consists of the attribute-based and the data-item-based
focal points. The user can refine the two-dimensional focal points
to obtain the chart and the text that explain the data facts found ina more focused portion of the dataset. The efficacy of the proposal
was assessed through a quantitative and qualitative evaluation using a prototype visual analytics tool that employs the idea.

In recent years, we see a growing number of visual analytics systems employ the combination of visualization and text synthesis
technologies [1, 7–10, 13, 16, 17]. One of the objectives of such
systems is to achieve data exploration and data explanations at thesame time. The data visualization technology encourages users’
free information-seeking behavior, while text synthesis technology
summarizes the facts retrieved from the dataset using data mining
techniques. This approach assists people understand the data by
presenting visual and textual explanations of the statistical facts
that are retrieved from the dataset via data mining techniques. In
this way, such techniques can help users who are not trained invisual and statistical analysis to explore the data.
One of the challenges for “exploranation (= exploration + explanation)” technology[13, 21] is the scalability. The number of
attributes in the table datasets treated in the past research studies
was limited to a few. The problem with the existing proposals is thatthe size of the generated text is expected to grow in a polynomial
order of the number of attributes. For example, the synthesis result
for the SPI dataset with 51 basic indicators comparing UN member
states is a huge text consisting of 391,273 words.
The goal of this study is to realize an exploranative system
for large table data with many attributes. Specific challenges include, in addition to the already mentioned scalability, the ability
to present an overview and focus on interesting regions, support
for exploratory behaviors, identification and comparison of data
items, and support for collaborative work.

ACM Reference Format:
Natsuki Hosokawa, Kohei Arimoto, and Ken Wakita. 20XX. A Scalable“Exploranation” Technique for Hierarchically Indexed Table Data. In VINCI’20: The 13th International Symposium on Visual Information Communication
and Interaction, December 08–10, 2020, Eindhoven, The Netherlands. ACM,
New York, NY, USA, 5 pages. https://doi.org/10.1145/XXXXXXX.XXXXXXX
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ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. . . $15.00
https://doi.org/10.1145/XXXXXXX.XXXXXXXINTRODUCTION

VINCI ’20, December 08–10, 2020, Eindhoven, The Netherlands

Hosokawa, Arimoto, and Wakita

We focused on the fact that the attributes and data items of large
scale table data are often organized in hierarchies. In this research,
we took the approach of data and text summarization based onthese hierarchical structures. Specifically, the approach is based on
the “two axes of focus’, one in the dimension of attributes and theother in data items. We also offer an interaction technique called“two-dimensional drill down,” which allows the user to shift the
focal point of analysis along the two axes.
The efficacy of the proposal was assessed quantitatively andqualitatively using a prototype visual analytics tool for the Social
Progress Index[15] called SPIViewer1 system.

DC1 (Scalability) The proposal automatically generates charts
and text to illustrate the data to aid the visual exploration of theuser. Because the amount of noticeable data facts increases combinatorially as the number of attributes increases, a naive applicationof conventional methods produces a huge amount of charts andexplanatory texts as the volume of data increases. The former, i.e.,
the number of visual elements displayed, is serious but various
studies have been conducted in the past, including sophisticateddrawing methods, data clustering, multi-layered data, and the adoption of interaction. The latter, i.e., the generation of explanatory
texts, however, is less studied.

2 RELATED WORK
2.1 Automated insight systems and natural
language processing

DC2 (Overview and Focuses) Large-scale data analysis involves
overview tasks, such as summarization, dissectioning, and discovering patterns. Additionally, it also involves focused tasks related to
specific areas of the data where the analysts have a prior interest.

Automated insight systems employ data mining to extract importantfeatures called data facts. Tang and others [18] extract k-importantdata facts from data; Demiralp and others [3, 4] proposed a visualization technique of data facts. Automated insight systems provide
the general user with an overview of large and complex data.
Microsoft Power BI [12] and Google Sheets [5] combine automated insight systems with natural language processing and information visualization. The method generates charts and a texualexplanation of the data facts. Also, Quill [2], Wordsmith [11] are
Web browser plugins that enhance natural language generation(NLG) capabilities on such systems.

2.2

Natural text generation

To compensate for the shortcoming of the difficulty of grasping
the overview of the data through systems based on automatedinsight systems, Latif et al. have proposed VIS Author Profiles forbrowsing co-authorship relationships of researchers, the interactive
Map Reports for two-attribute datasets representing geographic
distributions, and a visual analysis system for data assessed forreadability and maintainability with respect to program code [8,9, 13]. These systems attempt to explain the whole data rather
than individual data facts using template based natural language
processing technique. There is deep learning based technique togenerate high quality natural language text from a dataset[20],however it often generates statements with no facual basis.

2.3

Explorative Visual Analytics

The aim of this study is to build a visual analytics system that
integrates data visualization and automatic sentence generation
techniques for large, superficial datasets. White and Roth havepointed out eight features of an exploratory search system that isused to analyze large unknown datasets. They include unclear priorgoal setting, continuous exploration over the long term, learningand comprehension, collaborative analysis activities, etc. [19]3 SYSTEM
3.1 Design Considerations
Five considerations were explored in the design for this study.
1 SPIViewer: http://smartnova.github.io/spi/

DC3 (Exploration) In an exploratory analysis of an unknown data,
it is necessary to first grasp the overview and then, according to
the findings obtained from the overview, to elaborate the analysis
on the focused substructures[14].
DC4 (Identification and Comparison) Data analysis often requires a detailed examination of a data item at the focus. It is also
necessary to compare items with similar or contrasting features.
DC5 (Collaboration and Presentation) The ability to share thegenerated sentences and visualizations with other people is desired.
Such feature helps multiple collaborating explorers to break up thesearch process and share important information with each other.

3.2

System Overview

The data items and attributes of many large-scale table statistical
data, are often hierarchically organized. In dealing with scalability(DC1), we employed the notion dual focalization axis in which theanalyst sets the focus for each hierarchy of items and attributes, andavoids the explosion of text volume by generating concise text withthe focus in the hierarchical structure as the context. To handle thedual focalization axis, the system’s hierarchical discovery function
was designed to support the two-dimensional drill-down mechanism(DC2, DC3). To aid the analysis work, where an analyst’s interest
shifts from one scope of data items and attribute set to another, thetext and illustrations that describe the relevant scope is reflectedin the display. The system provides the ability to compare pairsof data items and attributes (DC4). Finally, the ability to save andshare the status of the analysis at any point in time is offered (DC5).

3.3

Extraction of Data Facts

Statistical features extracted from a dataset, such as extremums
and outliers from the overall distribution, are called data facts [18].
The automated insight system generates sentences that explain thedataset by describing the k-most prominent data facts. In contrast,
this study generates natural language texts based on three kinds ofstatistics: extremums and outliers, following [8].
For extraction of one-dimensional outliers, the range of thefirst and third quartiles is taken [6]. For the extraction of twodimensional outliers, we select attribute pairs that has a sufficiently
large (> 0.7) absolute values of correlation coefficients and thesecond PCA-component being a one-dimensional outlier.

A Scalable “Exploranation” Technique for Hierarchically Indexed Table Data

Egypt

Figure 2: Regions and Attributes compared on the data visualization view (Northern Africa vs. Africa and Access to
Advanced Education vs. Overall SPI)

3.4

Data Visualization

SPIViewer is a prototype system of the proposed idea. It is a visual
analytics tool for the SPI data. In the map view (Figure 1-○)
B it
depicts the map of the reginal focus. It illustrates the UN member
states in the regional focus (DC1). The colors illustrate the theirratings in the three main SPI components, e.g, Basic Human Needs,
Foundations of Wellbeing, and Opportunity (DC2). For example,
from the characteristic reddish color given to Egypt, its higher levelof Basic Human Needs is observed (Figure 1). The prominence of
this country in comparison with others is recognized, too.
The bar chart displays the attribute values of all the member
states and highlights bars of in-focus region (northern Africa; Figure 1-○;
D DC4). When two attributes are compared, a scatterplot
illustrates the data dsitribution. When two regions are compared
simultaneously blue, pink, and gray colors are used to depict the
1st region, 2nd region, and the rest (DC4; Figure 2).

3.5

Text Generation

The system converts the extracted data facts, into textual explanation by a text template. According to the kind of the selected data
items, the system employ three kinds of templates.
Case 1 (when a single data item is selected): The path startingfrom the topmost data item in the hierarchy down to the selected
item is regarded as a series of gradually narrowing the dataset. For
example, when Germany is selected, the path starts from the topmost element (all UN member states), European countries, Western
European countries, and finally to Germany.
By comparing the selected item with a group of higher-level
items along such path, the characteristics of the selected item canbe described in the broader context. For example, we compare
Germany with all UN member states, then with European countries,
and finally with Western European countries, to generate a series ofdescriptions on Germany situated in different geographical scopes.
A simple application of this technique would generate redundant text. For example, Germany is a world leader in 10 indicators.
Naturally, Germany also beats other European countries in these
indicators. To avoid repeaded generation of redudancy explanation
due to those self-evident data facts, SPIViewer suppresses repeatedreferences to the same attributes 2 .
Case 2 (when an element in the middle tier in the hierarchy
is selected): Firstly, among the selected data items, one that outperforms others is introduced. For example, suppose (Southern
Europe, Basic Human Needs) are selected for regional and attribute
focuses, respectively. Here, the generated text introduces Portugal
as it marks the highest Basic Human Needs value among Southern
2 Click on

and your Web browser shows the SPIViewer.

VINCI ’20, December 08–10, 2020, Eindhoven, The Netherlands

Europe countries . Then the text continues with comparison of
the selected element with its siblings. With the SPI example, thesystem conducts a comparison of Southern Europe with others (i.e.,
Northern-, Eastern-, and Western European countries, respectively).
Often the extracted data facts are related with many attributes.
Enumerating them all may result in a long sentence. In this study,
lower-level attributes are hidden but a toggle unhide/hide switch isoffered for the user to see them all.
Case 3 (when two sets of data items (group A and group B)
are selected): From the comparison between them, we can split
the set of attributes into three groups: a set of attributes that group
A (B) is noticeably better than B (A), and the set of other attributes.
We provide five text templates according to the ratio of these three
attribute groups to improve readability (DC1,2). The details areomitted due to the space limitation.

3.6

Interaction

SPIViewer is a Web-based VA tool for the SPI dataset (Figure 1). The
region selection pane ○A presents the regional hierarchy in a tree
view (DC3). The map pane ○B automatically zooms in/out on theselected region in response to the selection of the current region.
The analyst can also click on and select a UN member state fromthe map pane. Selection of different geographical region updates
the textual description in the explanation pane ○.
E Using “Comparemode” toggle switch in the region selection pane, the user can see
and read the comparison results in the chart and the text (DC4).
The attribute selection pane ○C provides a tree view similar to
the region selection pane, which allows the analyst to browse theattribute hierarchy and refine the selection of the attributes. The
SPI dataset consists of 51 statistical attributes and 15 aggregate
attributes that group together statistics of lower-level attributes.
The value distribution of the selected attribute for UN member
states are depicted in a bar chart in the data visualization pane○.
D Below the figure, a brief description of the respective statistics
and a link to the source of the information are displayed. The
data visualization pane offers similar comparison capabilities tothe region selection pane. By using the toggle switch, the analyst
can compare two different statistics in a scatterplot and see thecorrelation among them.
Tools are provided in the upper right corner to assist in the browsing process ○.
F The system records changes in two-dimensional
focus for bookkeeping purpose. The “<” and “>” icons instruct thesystem to move backward and forward, respectively, along withthe browsing history. The “Save” button saves the snapshot of theanalysis by assignment of a separate URL to the two-dimensional
focus settings. The user can duplicate the view by opening a newbrowser tab and use the URL for bookmarking and sharing/posting.

4 EVALUATION
4.1 Use Case
Suppose at the original globe view , we are interested in a northern African country with a characteristic reddish color. Clicking
it, the two-dimensional focus moves to (the selected country, allattributes). As a result, the contents of the panes are updated toreflected the selection . With the help of the hover tool, we learn
the selected country is Egypt and its SPI value is 61.71. The theVINCI ’20, December 08–10, 2020, Eindhoven, The Netherlands

Hosokawa, Arimoto, and Wakita

Table 1: The size of the explanation text generated for eachselection of the member state and the SPI index
Rank
1
1,956
3,556
6,159
9,629
12,261

Member state
Egypt
Argentina
Denmark
Italy
Ireland
Greenland

Index
Social Progress Index
Access to Basic Knowledge
Biome protection
Internet users/Population
Homicide rate/Population
Social Progress Index#words
122
31
30
22
21
11

explanation pane explains that “Egypt scores highest in Africa
on Access to Advanced Education, while worst on Outdoor air
pollution attributable deaths. Clicking the latter in the explanation pane, the two-dimensional focus shifts to (Egypt, Outdoor air
pollution attributable deaths). The updated data visualization paneshows that the severity of the situation for Egypt is outstandingin comparison with other countries. We use the “<” button to movebackward, then click on Access to Advanced Education to findthat Egypt ranks highest in Africa, and 39th worldwide . Using
the comparison function, we see the indicator and the SPI overall
index are linearly correlated but that Egypt stands out in this global
trend in offering a superior level of higher education (Figure 2).
Here, our interest shifts to trends in Africa rather than just Egypt.
Selecting Africa in the item selection pane, the two-dimensional
focus moves to (Africa, Access to Advanced Education). The bar
chart tells general poor performance of African countries on thisindicator but that a few excellent countries including Egypt.
From the above explorations, we learned that basic living conditions and advanced education are good in Egypt but it is challenged
with respect to welfare, most seriously by air pollution. For the
moment we go back to the point where this exploration started(Egypt, all attributes) and used the “Save” button to bookmark this
exploration . We send this URL to a colleague and ask for further
research, using our research result as a starting point.

4.2

Scalability

The SPI dataset was used to assess the scalability of the proposal.
First, we naively enumerated meaningful data facts from the combination of all countries and attributes, then converted them all into
a textual explanation, and measured its volume. The text consists of
367,476 words and is beyond quick comprehension for the human.
With our text reduction techniques, the size of the text generated
is expected proportional to the product of the number of data facts
and attributes. The longest text is expected to be generated fromthe combination of data items and attributes that has the largest
number of child elements in the hierarchies. This means that the
text size is independent from the height of the hierarchy.
The average number of sentences generated was 24 (Table 1),
with the longest being (Egypt, SPI). 3,234 focuses resulted in the
shortest sentences of length 11 words. Their significantly shorter
lengths are due to missing data in the dataset.

4.3

Task-based Analysis

We conducted experiments using the Japanese translation of SPIViewer.
All the participants are Japanese, computer science students andknowledgeale of visual analytics. We performed a comparison between SPIViewer and Google Spreadsheets with eight tasks (https://j.mp/3lbuL3Q). The tasks starts from a simple identification andcomparison task to more complex one: “Which of the two distributions of opportunities for social advancement scored higher overall,
Africa or Asia?”. Participants were asked to learn from three tutorial
videos (SPI data - 2 minutes 3 seconds, SPIViewer - 6 minutes 26 seconds, and Google Spreadsheet and its automated insight function 5 minutes 36 seconds) before the experiment.
Average working time was 15.4 minutes on SPIViewer and 35.3
minutes on Google Sreadsheet. A strong correlation in the distribution of working time was observed: participants completed thetasks in half the time of using Google Spreadsheets. The experimentand the short interview session were recorded on video. A qualitative evaluation is now underway. In the interviews, the participants
provided the following positive comments about SPIViewer: “Thestructure of the screen is easy to understand and operate - (X2,
X3),” “SPIViewer was much easier than a spreadsheet because it
does not require me a formula - (All),” “Spreadsheets are a painin the ass to visualize,” and “SPIViewer is Fun! - (X4).” Some of
the respondents asked for improvements in the software: “I want
the ability to search for a country (All),” “I wish the text was moredetailed - (X4, X7),” “I want the scatterplots to have the same aspectratio - (X3, X6, X7),” and “I need a better color scheme - (X2).” Moredetailed qualitative analysis is undergoing.

5

DISCUSSION

The text generated by the proposal explains outstanding attributes
for each data item. It is easy to understand the characteristics ofthe data items. However, comprehending attributes-based facts isdifficult. This issue should be addressed by a text generation where
attributes are explained by outstanding data items.
We have observed that certain interesting facts were overlookedby the outlier detection techniques employed in this research. For
example, Egypt’s outstanding color among neighboring countries
visually the human but the system failed to suggest it.
Mumtaz and others stress the importance of consistent linking
across the triad of data visualization, text, and word-sized charts
embedded in the text [13]. From their argument, the SPIViewer canbe improved upon in a few directions.

6

SUMMARY

A growing number of data analytics system incorporates the combination of data visualization and automated document generation.
One of the contributions of this work is the identification of challenges for such systems to deal with large scalable datasets. We
have shown that the challenges can be solved by exploiting thehierarchical structure often found in large table datasets. The effectiveness of our method was demonstrated through SPIViewer.
Future work includes more detailed user studies, application toother datasets, support for time series analysis, and provision of aricher user interface for exploratory analysis.
Acknowledgments The authors would like to acknowledge Teikoku
Databank, Ltd’s Center for TDB Advanced Data Analysis and Modeling for providing financial support.

A Scalable “Exploranation” Technique for Hierarchically Indexed Table Data

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