A Visual Analysis Tool That Smoothly Switches
Between Tabular Forms and Parallel Coordinates
Kazuo Misue

Takashi Yuki

Department of Computer Science
University of Tsukuba

Department of Computer Science
University of Tsukuba

misue@cs.tsukuba.ac.jp
ABSTRACTAn intermediate representation between tabular forms andparallel coordinates is proposed. The new representation
makes it possible to switch back and forth between tables
and parallel coordinates in smooth transformations. The
authors have developed a tool that can manipulate these
representations for the analysis of multidimensional data.
Through experimental evaluation, the advantages of thistool are illustrated. It is expected that both the representation and the tool offer a significant improvement to theearly stage of data analysis.

Categories and Subject Descriptors
H.5.2 [Information Interfaces and Presentation]: User
Interface—graphical user interface, interaction styles

General Terms

Figure 1: Example of intermediate representation
between tabular forms and parallel coordinates.

Design, Human Factors

Keywords
Information visualization, visual analysis, multidimensionaldata, parallel coordinates, tabular form

1.

INTRODUCTION

A variety of visualization methods are used in data analysis processes. Tabular forms are often used as the fundamental representations in the early stage of data analysis.
Parallel coordinates[3] are one of the most widely used waysof visualizing multidimensional data. That is, data in a table are commonly translated into parallel coordinates during
the course of an analysis.
The purpose of our study is to make analytic tasks thatinvolve multidimensional data more effective. Our premiseis that combining a tabular form with parallel coordinates
would be useful if a translation from tables into parallel coordinates is performed.

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The initial challenge, however, is that they are quite different representations, which can make it difficult for a given
representation to play a role as a table and also as parallel
coordinates.
We, therefore, propose a process of switching between atabular form and their corresponding parallel coordinates insmooth transformations that preserve their common characteristics. To this ends, we developed an intermediate representation between a tabular form and parallel coordinates(see Figure 1). These two representations can smoothly
switch back and forth between each other via the intermediate representation. This paper describes that intermediate
representation as well as the tool that manages this task.

2.

BACKGROUND

2.1

Data

The object data of the analysis that we assume is multidimensional data. The multidimensional data consists of one
or more, but usually many, data sets. Each data set has nvalues (n  1).

2.2

Table

In a table, in most cases, each value is expressed by acharacter string in one cell, and one data set is expressed ina row of cells on a horizontal line. The values in each dimension are arranged vertically. We can traverse the values ofa given dimension by following the cells vertically. We may
also change the order of the dimensions appropriately.
The fundamental model of tables is based upon two-dimensional matrix placement. This model enables many people to understand data without the need for a detailed explanation.

representation of dynamic networks, in which a snap shot ofa changing network is expressed in each cell.
Our focus here is on tables that represent each value ina cell. Our aim is to create a table that corresponds to agiven set parallel coordinates by making use of the fact thattables are often used with parallel coordinates.

2.3

3.2

Parallel Coordinates

In a set of parallel coordinates, each dimension is expressed on a vertical axis. Each value is expressed at theposition of a point on an axis, and one data set is expressedby connecting the points on all axes using line segments. We
can grasp the distribution of the values of a dimension byobserving the placement of the points on the axis. We may
also appropriately change the order of axes that correspondto the dimensions.

2.4

Comparison between Tabular Form andParallel Coordinates

In tables, values are expressed by character strings thatmake it possible for us to precisely read the values in detail.
It is not easy, however, to read an overview such as the
distribution of values in this way. For this reason, a table isunsuitable for an analysis based on an overview of values.
Parallel coordinates visually represent the distribution ofvalues and the correlation between dimensions so we canintuitively gain an overview of values. That said, it is noteasy to read every value precisely. Detail data may be hidden
by other data depending on the distribution of values.
Both tables and parallel coordinates are widely used representations in data analysis. They have different features,and the drawbacks of one are compensated for by the advantages of the other.

4.
3.

RELATED WORKS

Extension of Parallel Coordinates

Various studies have been performed whose aim has been
the extension of parallel coordinates. In some studies, parallel coordinates were extended with the principal aim ofpromoting the efficiency of multidimensional data analysis.
Other studies developed representations that adopted selected features of parallel coordinates for a number of different purposes. Still others have tried to combine parallel
coordinates with some other representations.
Yuan et al. combined parallel coordinates with scatterplots[9]. A scatter plot is drawn in any area between two
axes. Parallel Sets is a method of visualization for categorical data[5]. This method is based on an axis layout ofparallel coordinates, but shows data frequencies instead of
the individual data points. Parallel Tag Clouds is a combination of parallel coordinates and tag clouds[2]. Tags arelaid out on implicit axes and links are drawn as moderatelines, an approach that makes the comparison of sets of tags
more easy. Lex et al. developed the Caleydo Matchmaker,
which facilitates the comparative analysis of multidimensional, quantitative data[6]. It uses a parallel coordinates
metaphor, and uses head maps rather than simple lines asaxes.
Our aim here is to introduce the features of parallel coordinates into a table by making the arrangements of cells
more flexible.

INTERMEDIATE REPRESENTATION

Visualization is a widely used and effective approach todata analysis. Many visual representations have been proposed that are in use. In this section, we introduce some
important works related to table and parallel coordinatesthat are relevant to this study.

We designed an intermediate representation between tabular form and parallel coordinates. This representation canbe partially in the table form and partially parallel coordinates. We can smoothly switch back and forth between
visualizations in the two representations via the intermediate representation.

3.1

4.1

Extension of Table Representation

In the initial stage of most data analysis processes, a table
is used as the format for the arrangement of data[1, Chapter 1]. In typical tables, character strings are arranged incells and do not always assume a visual form of expression.
However, tables do possess an aspect of UI for a large-scale
database.
With the aim of improving the power of table expression, a number of studies have expanded tabular forms while
maintaining the basic placement style. TableLens enables
users to intuitively grasp large-scale multidimensional data
sets by changing the sizes of cells according to the user’s
viewpoint and arranging charts in cells in order to visualize the values[7]. John et al. expressed data in TableLens
by using a transparency[4], which made it possible to grasp
more detailed information. Telea combined a table with atreemap[8], which made it possible to grasp the values andthe hierarchical structure related to them. The arrangementof various representations in tabular form is another way ofcreating an extension of a table. A scatter plot matrix is an
example of such an extension. Another example is matrix

Design of Intermediate Representation

In a table, one of the most widely used ways is to express
each data set by lining up cells horizontally. In this way,values in the same dimension are arranged vertically. In
parallel coordinates, each value is expressed at the positionof a point on a vertical axis. Both representations express
a dimension in a vertical alignment. Our concept was todesign an intermediate representation by focusing upon thiscommon feature.

Expression of a Value.
In the same way as in an ordinal table, a single value isexpressed by a character string in a rectangle (a cell).

Expression of a Dimension.
In the same way as in a table, a dimension is expressedby arranging cells vertically. No axes are explicitly drawn
as parallel coordinates, though the result is the same as inparallel coordinates in that a dimension is expressed in a
vertical arrangement. We can call such an arrangement a“virtual axis.”(a) Initial table view.

(b) All axes have been rearranged.

(c) Cells on the category axis have been rearranged.

(d) All cells have been rearranged.

Figure 2: Intermediate representation between tabular forms and parallel coordinates.

Expression of a Data Set.

Spatial Selection.

In the same way as in parallel coordinates, a data set isexpressed by connecting cells that are in two adjacent dimensions. When two cells are adjacent to each other spatially,no line segments are drawn between them.

This way employs the use of a rubber band. The userdraws a rubber band by dragging with the mouse, after
which cells inside the rubber band are selected. Spatial selection is suitable for selecting cells according to their positions.

4.2

Switching Representations

We allow some degree of freedom in regard to the intervals of virtual axes and placement on the axes. When weremove the interspaces between axes, and re-arrange cellson the axes according to a certain order of the data set, wethen obtain a table. When we expand the intervals among
the axes appropriately, and re-arrange the cells on the axes
according to their values, we then obtain a set of parallel
coordinates.

Logical Selection.

5.

Selected elements are highlighted. Selected cells are highlighted in blue, and lines related to those cells are highlighted
in red. This makes it easier to find data sets, including theselected cells.

DEVELOPMENT OF A TOOL

We have developed a tool that provides the following functions for the management of tables, parallel coordinates, andthe intermediate representation.

5.1

Initial View

The tool arranges all cells on a grid immediately after
reading the data sets (see Figure 2(a)). In other words, thedata is represented in tabular form.

5.2

Selection

The tool provides two ways that cells can be selected:
spatial selection and logical selection.

The logical structures of a table and parallel coordinates
are used. The first click on a cell activates selection of thecell. The second click on the cell activates selection of allcells in the same dimension. The third click activates theselection of all the cells in the same data set. The fourthclick activates the selection of eight neighbors of the cell.

5.3

5.4

Highlighting

Transformation

The tool provides several kinds of transformations via operations that connect a tabular form and parallel coordinates.

Horizontal Transformation.
We may change the interspaces between the virtual axes
and the order of the axes via manual operations (see Figure 1). Moreover, we can use a command to rearrange all theFigure 4: Example of a temporal table.
(a) First clickaxes at the appropriate intervals (see Figure 2(b)). When
we use this command, the transformations of the view are
shown in animation.

Vertical Transformation.
We can use another command to change the positionsof the cells on an axis (see Figure 2(c)) and yet use another command to change their positions on all axes (seeFigure 2(d)). If we rearrange all the axes in appropriate
positions and change the positions of all cells according totheir values, we then obtain a set of parallel coordinates.
(b) Second click.

Temporal and Partial Table.
We can create a partial table by collecting data sets thathave the same value as a specified cell. Related cells are
made to come together to the specified cell via a hovering
operation that is done with the mouse. Figure 4 is an example of such a representation that includes a temporal table.
This function works as a tool tip, so it is quite easy to createa table in which we can see the original data set.

5.5(c) Third click.

Two adjacent dimensions can be grouped into one dimension, an operation that makes it possible to reduce the number of dimensions. When the positions of cells express theirvalues, the position of a pair of grouped cells follows one in
the pair.

5.6(d) Fourth click.
Figure 3: Logical selection. Figures on the left-handside are in tabular forms and those on the right-handside are in parallel coordinates.

Grouping

Shifting

The Shift operation moves cells that are related to a specified cell (see Figure 5). It is similar to the logical selection
with highlights given above, but it also changes the positionsof cells. This provides more impressive and useful visual effects by combining the highlighting of cells.
Because the Shift operation creates a subset of cells, itworks as a kind of filtering. When we want to focus upon asubset of cells with particular properties, we can then createthat subset spatially.

6.

USAGE EXAMPLE

Here we describe the tool being applied to a practical example.

6.1

Objective Data

Objective data was extracted from a sales database managed in our laboratory. While it is not the database of an

Figure 5: Cells related to tea has been moved using
the Shift operation.

Figure 6: Values on the time axis have been rearranged.

actual shop, the transactions whose records were used are
real.
Each record represents a single transaction that has six
attributes: purchase date, time, customer name, item name,item category, and price. The customer name, item name,and item category are categorical data. The other three
attributes are numerical data.
We used data that were recorded between May 22 andJune 7, 2009. The number of records was one hundred, andthese records represent ten different customers. In addition,
the item name attribute included twenty-one different kindsof items.

6.2

Purpose of Analysis

The aim of the analysis was to gain insight into the purchasing tendencies of each customer.

6.3

Operations

After reading the data, the tool displays the data in atable. From that table, we understand that there are six
kinds of attributes in the data, and we can read the values
for each attribute.
First, we focused our attention on the time and then proceeded with the analysis. We changed an attribute of time
into parallel coordinates in order to visualize the distribution of the times when items were purchased (see Figure 6).
We were able to see that the distribution according to time
was divided into a number of separated by several gaps. The
small gaps indicate that items not purchased at midnight,
and the large gap indicates a holiday. Such features cannot
be readily ascertained from a table, but can be immediately
found once the values are placed on a time axis. If we notesome interesting features of the distributions, we can easily
proceed with a more detailed analysis of those features.
We transformed all attributes into a set of parallel coordinates so that the analysis could expand to consider not only
time but all the other attributes as well. We understoodthat tea and coffee were the predominant purchases due tothe darkness of the line that could be seen when we focusedupon category. We compared the purchase times of bothcategories in order to gain insight into the time periods inwhich they sold well. We obtained the time when tea soldwell by using the Shift operation. When we selected the cellof category tea and moved it to the left, the cells of the purchase time of tea also moved to the left. We were then easily

Figure 7: Cells related to tea and coffee have been
moved using the Shift operation.

able to see that several bottles of tea had been purchased
every day.
When we also moved coffee to the right-hand side, we
found that coffee had not been purchased every day, but
had been purchased in relatively greater quantity at sometime (see Figure 7). We could easily compare the differencesin the related data by performing the Shift operation andmoving back and forth between these two categories.
The cells left on an axis after performing the Shift operation twice were of the data related to categories except
for tea and coffee. In these data, we could then observe the
distribution of the entire data set. This shows that we wereable to gain insight into an overall set of information by focusing our attention only on certain subsets. For example,
judging from the entire data set, many items seemed to bepurchased every day. However, when we focused on the categories of coffee and tea, we found that only one item in theother categories had been purchased on each of the otherdays. In other words, we found that coffee and tea actuallyaccounted for many of the sales in the entire data set.

7.

EXPERIMENTAL EVALUATION

We performed an experimental examination to evaluate
the effectiveness of the representation and the tool we developed. We performed the same tasks both with our tool
and a comparison tool, and compared the results in termsof time and accuracy.

Table 1: Tasks performed in the examination.
Questions1
How much does the most expensive item cost?
What is ranking of beverage on the basis of sales?
What is the item whose sales ranking is 6?
What is the most sold item among items priced at 200 yen?
What is the most sold item in the category with a sales ranking of 3?
Who bought the maximum number of Harvest Cacao?
Who bought the item that was sold the most among the lowest priced items?
Among the people who purchased Iyemon Tea, when did the person who bought the fewest
items make the purchase most?
#9 Who bought the maximum number of the most sold item in the most sold category?
#10 Between C and D, which was the one that bought items regularly?
#11 Between tea and beverage, which item was sold regularly?
#12 What is sales ranking of BOSS Rainbow Mountain among all items?
#13 When was the time when the first product was sold?
#14 What are the rankings of the items purchased by H (arrange in increasing order)?
#15 Who bought the maximum number of the items that had a sales ranking of 2?
#16 What is the most sold category in the purchase history of E?
#17 What is the most sold item in the category with a sales ranking of 2?
#18 Which item did the person who bought the maximum number of 200-yen items buy the most?
#19 Who bought the most items in the category to which the second-sold item belongs to?
#20 On which day was the most sold item in coffee sold most?
#21 Between E and F, which was the one that bought items regularly?
#22 Among the items priced at 200 yen and 80 yen, which items were sold regularly?
1 All questions were in Japanese.
2 The number of attributes required focus.
3 Category of tasks: “s” denotes value-specifiying tasks and “c” denotes value-comparing tasks.
No.
#1
#2
#3
#4
#5
#6
#7
#8

7.1

Attr.2
1
1
1
2
2
2
3
3

Cat.3
s
s
s
s
s
s
s
s

3
2
2
1
1
1
2
2
2
3
3
3
2
2

s
c
c
s
s
s
s
s
s
s
s
s
c
c

Comparison tool

We also prepared a tool to perform comparisons. This tool
provides an exclusive view of each table along with a set ofparallel coordinates. The tool has fundamental functions foreach table and its parallel coordinates. Linking and brushingis provided to make the correspondence between the twoviews clearer.

7.2

Tasks

We prepared twenty two tasks that had varying degrees ofdifficulty and characteristics, but all of which had unambiguous answers. Table 1 shows questions in all twenty tasks.
There were two kinds of tasks: value-specifying tasks andvalue-comparing tasks. An example of a value-specifying
task would be to pose the question, ”How much does themost expensive item cost?”; an example of value-comparing
tasks would be the question ”Between C and D, which was
the one that bought items regularly?”
Performing a task requires focus upon at least one attribute. We varied the number of attributes receiving such
focus from one to three so as to vary the degree of difficultyof the tasks. For example, if we can complete a task by focusing upon the price, the number of attributes of that task
is one. If we must focus upon the price and the category, thenumber of attributes is two. The latter task is, of course,
more difficult than the former.

7.3

Users

Our users were six undergraduate and graduate students
majoring in computer science. Because they had no experience with either tool, they were allowed to practice using
both tools prior to the experiment in order to become familiar with them.
Every user asked to perform eleven tasks with our tooland the other eleven tasks with the comparison tool. For a

Figure 8: Ratio of users who answered correctly.

task, three users performed with our tool and the other tree
users with the comparison tool.

7.4

Results

First of all, we focused on how many users could answer
correctly. Figure 8 shows the ratio of users who answered
correctly. The ratio for the comparison tool was 0.33 forsome tasks. The ratio for our tool was 0.66 or more for alltasks. The ratio for our tool was higher than or equal to theratio for the comparison tool for all tasks, except for one.
We attribute this difference to a users misunderstanding thecorrespondence between the two views in the comparison
tool. Indeed, a few users gave up before completing sometasks with the comparison tool.
Figure 9 shows the average completion time for each task.
For most tasks, the time taken with our tool was shorter
than the time taken with the comparison tool. For some9.

Figure 9: Task completion time.
tasks with two attributes, however, the reverse was true.
Because our tool shows the transformations through animation, we surmise that the overhead associated with theanimation resulted in a longer time for the tasks with two
attributes. For more complex tasks with more attributes,
the time taken in the case of our tool was shorter. The
overhead may be ignored for such complex tasks.

8.

CONCLUSION

In this paper, we have proposed an intermediate representation between tabular forms and parallel coordinates. This
representation makes it possible to switch back and forth between these two representations in smooth transformations.
We also developed a visual analysis tool that provides functions for the management of these representations. By performing a number of tasks that involved reading informationfrom a set of multidimensional data, we confirmed that ourtool offered advantages in terms of correctness and the time
taken to perform tasks. We anticipate that this will makeit possible to streamline and facilitate the early stage of theanalysis process for multidimensional data.

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