ExploroBOT: Rapid Exploration with Chart Automation

John McAuley

, Rohan Goel

1,2

and Tamara Matthews

CeADAR, School of Computing, Technological University Dublin, Kevin Street, Dublin 8, Ireland

BITS Pilani, Department of Computer Science, Goa, India

Keywords:

Visual Analytics, Automatic Chart Generation, Data Exploration, Intelligent Visual Interfaces.

Abstract:

General-purpose visualization tools are used by people with varying degrees of data literacy. Often the user

is not a professional analyst or data scientist and uses the tool infrequently, to support an aspect of their

job. This can present difﬁculties as the user’s unfamiliarity with visualization practice and infrequent use of

the tool can result in long processing time, inaccurate data representations or inappropriate visual encodings.

To address this problem, we developed a visual analytics application called exploroBOT. The exploroBOT

automatically generates visualizations and the exploration guidance path (an associated network of decision

points, mapping nodes where visualizations change). These combined approaches enable users to explore

visualizations based on a degree of “interestingness”. The user-driven approach draws on the browse/explore

metaphor commonly applied in social media applications and is supported by guided navigation. In this paper

we describe exploroBOT and present an evaluation of the tool.

1 INTRODUCTION

Exploration is the key to Visual Analytics (VA). Du-

ring an explorative session, an analyst can create a

large number of visualizations when trying to under-

stand the intricacies of a dataset. In this process an an-

alyst selects data dimensions and applies different en-

coding strategies to produce a range of visualizations.

Experienced analysts use this exploration as a means

of sense making – iterating through vast swathes of vi-

sualizations to understand data particularities. How-

ever, data analytics is practiced by a broader commu-

nity of users with different levels of analytic expertise.

Unlike professional analysts, business users may

lack formal statistical training or visualization expe-

rience and may only use a tool occasionally, for pre-

sentations and reporting, or to understand a dataset.

This results in users spending time learning a tool, un-

derstanding the process of visual encoding and man-

aging complex datasets. The infrequency in using

the tool can enhance the problem as they must reac-

quaint themselves to the process of visual exploration

on each use. As a result, users often rely on the same

chart type – the approach that produced a positive out-

come previously – and ignore the different encoding

strategies that could provide more meaningful results.

To address this problem, we have developed an au-

tomated visual analytics tool called exploroBOT. Its

aim is to replace visualization creation with visuali-

zation exploration, borrowing interactive metaphors

from social media applications (such as “liking” and

“swiping”). Thus, as opposed to creating visualizati-

ons for a dataset by relying on preferred visual enco-

dings, the user can explore a range of visualizations

and then decide on what type of visualization or type

of data they would like to explore next. This reduces

the complexity experienced during visual exploration

and enables the user to rapidly navigate large datasets.

In this paper, we describe the motivating princi-

ples of exploroBOT and outline a set of design con-

siderations with their implementation. In a controlled

experiment we evaluated exploroBOT with a leading

Business Intelligence application (Tableau), the re-

sults showing that users answered a set of tasks more

rapidly and more accurately when using exploroBOT

than when using Tableau. This suggests potential in

developing systems that encourage users to explore

rather than create visualizations. We discuss the ﬁn-

dings and outline directions for future work.

2 REVIEW OF LITERATURE

Numerous platforms enable data exploration through

computer-guided interactive visualizations, providing

visual analytics that target at speciﬁc business and

McAuley, J., Goel, R. and Matthews, T.

ExploroBOT: Rapid Exploration with Chart Automation.

DOI: 10.5220/0007345202250232

In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theor y and Applications (VISIGRAPP 2019), pages 225-232

ISBN: 978-989-758-354-4

225

analytic domains: Cognos (IBM), SQL Server BI

(Microsoft), Business Objects (SAP), Teradata, and

PowerPivot (Microsoft), many deployed as web ap-

plications

: Tableau, Spotﬁre, QlikView, JMP (SAS),

Jaspersoft, ADVIZOR Solutions, BoadrBI, Centri-

fuge, Visual Mining. However, the effective use

of these tools requires expertise in visualization,

statistics and data or relational semantics. There

can be a combinatorial number of visual encodings

(with multi-dimensionality and multi-level hierar-

chies) available to a user. This can provide the user

with a challenging visualization design space and re-

sults in sub-optimal or ineffective representations.

As a result, research seeks to support user-driven,

ﬂexible exploration through the application of “intel-

ligent interfaces” (Pak Chung et al., 2000), Automatic

Visual Analytics enabling guided exploration (algo-

rithmically driven, adaptive and personalized) (Stol-

per et al., 2014; Graham and Wilkinson, 2010), “visu-

alization recommendation” (recommender systems)

and automated “visual assistants”.

Recommender systems incorporate a user’s pre-

vious activity on a platform to recommend new con-

tent. VisComplete for example, uses an existing col-

lection of visualization workﬂows (“pipelines”), mo-

deled as directed acyclic graphs, to build new pipeli-

nes and predict likely choices. The “small-multiples”

method is used in various multi-dimensional visuali-

sations (Scheibel et al., 2016). The “behavior-driven

recommendation” implemented in HARVEST (Gotz

et al., 2010) monitors users’ behavior patterns (“active

trail”) to derive recommendations.

In the absence of an item-user history, recommen-

ders use statistical and perceptual measures organi-

zed upon various conceptual frames. VizDeck, for in-

stance, generates a user interface that mimics the card

game metaphor, visualizations being selected based

on statistical properties of the data (Perry et al., 2013).

The SEEDB is a data-driven recommender that ﬁnds

interesting representations based on a “distance me-

tric” from a reference dataset (Vartak et al., 2015b).

VizRec (Vartak et al., 2015a) is a knowledge-based

visualization recommendation system using a set of

relevant criteria (called “AXES”). Although the sy-

stem can generate interesting visualizations fast, the

approach requires manual preprocessing to create uni-

ﬁed visualization models using the AXES technique.

Voyager (Wongsuphasawat et al., 2016) supports

“faceted browsing”, introducing the notion of “design

and data variation”, along with “demonstration as a

www.tableau.com; www.spotﬁre.tibco.com;

www.qlik.com; www.jmp.com; www.jaspersoft.com;

www.advizorsolutions.com; www.board.com;

www.centrifugesystems.com; www.visualmining.com

means to recommendation” while VisExamplar (Sa-

ket et al., 2017) proposes visual transformations ba-

sed on a user’s interaction with an existing set of vi-

sual encodings (predicting users’ intent).

An area that received less attention in the litera-

ture is the idea of visualization sequencing in which

users can navigate a dataset using a set of (semi or

automatically generated) visualizations sequenced in

a coherent presentation. The notion of coherence is

addressed in “GraphScape” (Kim et al., 2017), which

looked at the cost of sequencing visualizations based

on chart similarity. Although this is an interesting

approach considering the underlying design space, it

neglects the user’s comprehension of the data or their

requirements in moving from one chart to another.

Visual Assistants provide automatic visualization

based on a combination of knowledge or heuristics

(based on early work in visualization research that

reduced abstract complexity through development of

data and user-orientated taxonomies (Shneiderman,

1996; Amar et al., 2005). “Vizassist” uses Interactive

Genetic Algorithms (IGA) to map models for visuali-

zation heuristics learning from user feedback (Bouali

et al., 2016). However, the user is required to ma-

nually deﬁne the IGA mapping model – therefore to

understand the core tenets of visualization design and

practice. In “CEF” (Yalc¸n et al., 2016) design gui-

dance links to cognitive stages, has ﬁxed visualizati-

ons per data type and enumerates exploratory paths.

Several visualisation metaphors learned from this

study: the guided exploration, “faceted browsing”,

“design and data variation”, distance measures to eva-

luate chart “interestingness” – were creatively built

into exploroBOT.

3 THE exploroBOT SYSTEM

We present exploroBOT, an easy-to-use tool that ena-

bles the visual exploration of data without manually

creating charts or narrative development. The aim of

the system is to challenge the existing conventions on

visual analytics – in which the shelf and drag-drop

metaphors are widely applied – by automating the

chart creation and supporting data exploration pro-

cess. In this section, we consider the design space

and describe the implementation of exploroBOT.

exploroBOT is based on the “browsing explora-

tion” metaphor and uses a novel type of user-driven

navigation. It is supported by a visual guidance lat-

tice and a set of general decision-making steps, which

do not require previous learning from the user-system

interactions. The visual guidance lattice shows the

graph (network) of (currently available) charts inter-

IVAPP 2019 - 10th International Conference on Information Visualization Theory and Applications

226

Figure 1: Example of exploroBOT decision lattice.

connected by their navigation paths (Figure 1), pro-

viding visual clues of the seen/not seen charts and

the actual position along the exploration path. The

decision-making steps are generalized choices for the

next chart structure (design, dimensions, data type) or

special data properties (like “interestingness” and “si-

milarity”, which are described in Section 3.2).

The interaction in exploroBOT borrows from that

on social media archives, where users tend to con-

sume swaths of data quickly by honing their interests

in particular topics using simple interaction such as

liking, sharing, voting etc. Each of these lightweight

actions inﬂuence what content the user will be presen-

ted in subsequent sessions on the platform.

3.1 Path Selection during Exploration

The decision making process on which exploration

path to follow is entirely the user’s choice, suppor-

ted by a visualization of the charts network along the

“decision-making” steps (an example of the decision

lattice is shown in Figure 1).

The decision lattice is generated as a graph repre-

sentation of all the possible types of charts from the

data, with nodes representing the chart types. The

edges indicate the path choices between these charts

with the node colour indicating the current chart (yel-

low) and the possible charts in the immediate next

step (red) depending on the user’s choice. This de-

cision lattice serves as a “guiding” map for the user-

exploration of all possible charts.

While there is an advantage for experienced users

who already know the type of chart they want and can

navigate directly to a desired type of chart and data,

the less informed users can explore sequentially by

following several routes, deﬁned by their choices in

the “decision-making” steps. At each step, the user is

asked whether he ﬁnds the present visualization “in-

teresting”. If yes, the user can explore further by alte-

ring the following three properties of a chart:

• Number of Dimensions: add or remove a data di-

mension. For example, adding a numerical data

dimension to a box-plot generates a scatter plot;

• Type of Variables: Keeping the chart design and

the number of dimensions same, replace one of

the dimension by some other variable;

• Chart Design: Alter the visual encoding, without

changing the data

On the other hand, if the user is not interested in

the current chart, he is routed to the most “dissimilar”

visual encoding from the present chart, post which he

can start exploring again. The notions of “interesting-

ness” and “dissimilarity” are explained in the subse-

quent subsection.

This approach incorporates the concept of “user-

guidance” and also the user-chosen “steps” to de-

ﬁne the characteristics for each chart. The “user-

guidance”, based on a graph network for charts, crea-

tes a reference framework for understanding the path

and evolution of the exploration. It also helps in ﬁn-

ding important charts and how many or what types of

charts can be generated. The latter idea is a genera-

lization of the basic chart making process that a kno-

wledgeable user would apply: they choose the chart

type, number of dimensions and data type. These

combined approaches lead to a quick deﬁnition of the

required charts, without other inputs from the user in

deﬁning the design parameters.

3.2 Interestingness and Dissimilarity

exploroBOT is not a recommender system – where

previous information on user interest in certain charts

is recorded and then used to recommend more charts

or deﬁne the interestingness of a chart. Thus, here we

only look to fulﬁll the concept of chart “interesting-

ness” in its literal meaning.

The “interestingness” of a chart may have vari-

ous deﬁnitions depending on the user’s interests: the

user may be looking for charts with highly correlated

(or uncorrelated) data or for charts with high peaks

(e.g. outliers) or simply for multi-dimensional charts.

These criteria allow the user to quickly access the data

novelty or most unusual characteristics. While other

basis can be deﬁned as measures of “interestingness”,

the criteria used in this work to quantify it are:

(i) Data Correlation: Highly correlated data (iden-

tiﬁed using the Pearson correlation coefﬁcient) in

scatter plots and trend charts, hints towards an inte-

resting relationship between the two variables. A hig-

her absolute value of Pearson’s coefﬁcient will mean

a higher interestingness.

ExploroBOT: Rapid Exploration with Chart Automation

227

Figure 2: Examples of decision points in exploroBOT: ”In-

terested” vs ”Not interested”.

Figure 3: Examples of decision points in exploroBOT:

”Add/Drop data”, ”Change Data”, ”Change Design”.

(ii) Peaks: Spikes and large differences in a nu-

merical property of the dataset instantly attract atten-

tion. The normalized numerical differences in these

quantities are used to measure and compare the inte-

restingness value.

(iii) Outliers: Statisticians are looking for data

points defying the general characteristics of data. A

chart with more outliers is deemed more interesting.

When a user ﬁnds the current graph “not interes-

ting”, he is moved to a point in the decision lattice

with the most “dissimilar” graph to the current one.

To compare the charts, exploroBOT uses the cosine

similarity metric between visualizations represented

as vectors. For converting a chart to a vector, the fol-

lowing properties are considered: number of dimen-

sions, chart type (bar chart, pie chart, etc), markings

used in visualization (like bars in bar-chart, circles in

bubble chart, etc), types and names of attributes used

in visualization (numeric, string or date-time). Mo-

reover, “dummy coding” is used to represent the ca-

tegorical variables like “chart type” as dichotomous

variables, allowing computation of the cosine simila-

rity metric.

Nevertheless, it is not expected that users would

have a desired visualization in mind, the main scope

of exploroBOT is exploring the data within a well

organized framework, which shall contribute to the

user’s knowledge of data and its representations.

3.3 The User Interface

Given the aim of exploroBOT is to support non-

professional analysts, we developed a minimal inter-

face consisting of a single visualization and a single

decision point. This ensures a browse friendly inter-

face, as in social media applications. During the ex-

ploration process, on the screen (Figure 2 and 3), one

sees a chart and two buttons: “Interested” or “Not In-

terested”, along with a snapshot of the decision lat-

tice (as discussed in Section 3.1) to aid the exploration

process. A bigger view of the decision lattice (Figure

1) can be accessed by clicking on it.

As illustrated in Figure 4 each decision results in

a new graphical representation. In each instance, ad-

ding more data provides a more complex visualiza-

tion, encoding more data in an appropriate representa-

tion. In this example, the user begins with a pie chart

(univariate) with the attribute “Embarked”. Then the

user wants to explore further by adding a data dimen-

sion from many possible attributes. However, the user

only wants to see the “most interesting” visualization

that includes “Embarked” with one more dimension.

Here, the dimension “Fare” is automatically selected

(as the large difference in the heights of the “bars” is

a criteria for ”interestingness” – see subsection 3.2),

resulting in a bar chart (bivariate). Adding more di-

mensions, results in a grouped bar chart (multivariate)

and ﬁnally in a bubble chart. At each step in this ex-

plorative process, the new visualization or the new va-

riable to be added/removed is automatically selected

based on the concept of ”interestingness”. This pro-

cess reduces the need for a user to manually create a

visualization, select dimensions and measures etc.

When the user has completed the exploration, they

are presented with a history (Figure 5) of the visuali-

zations they interacted with during the session. At

this point, the user can change their reaction to a chart

(from interested to non-interested and vice-versa) or

potentially create a presentation. This approach ena-

bles iteration, re-engagement and path discovery.

4 DESIGN CONSIDERATIONS

In this section, we list the set of design considerations

(C1 - C5) which support the process of chart explo-

ration. The overarching aim is to create a simple

and easy to use application that enables a user to

intuitively explore large and complex datasets.

C1: Address the Cold Start Problem: Suggest

Dimensions for Analysis

Often there is a cold start problem with visual

IVAPP 2019 - 10th International Conference on Information Visualization Theory and Applications

228

Figure 4: Example of exploroBOT user navigation. Data from Kaggle, Titanic dataset (www.kaggle.com/c/titanic/data).

Figure 5: Example of History of charts the user has interac-

ted with (partial image).

analytics as people get to grips with a dataset and

consider what is of interest, what should they look at

ﬁrst or where should they start their analysis? This

problem is compounded when the data is complex,

if, for example, certain dimensions have a high

number of levels. In these cases, the user must bin

the data or ﬁlter out levels considered unnecessary.

Our approach is to suggest dimensions that can start

the analysis and provide the user with univariate

visualizations based on this selection – boxplot,

histogram, pie or bar chart. This reduces potential

overwhelm when beginning the explorative session.

A gradual increase in complexity helps the user

understand the data.

C2: Start exploration with Points of Interest

It is important that the initial visualization grabs the

user’s interest and incites their curiosity. From this

perspective, a set of statistical measures are used to

drive the analysis based on the idea of “interesting-

ness”. For example, outliers provide interesting focal

points which could enable the user to raise questions

or posit hypotheses about the data. Similarly, time

series help to set the scene or orientate the user, large

differentials within a single dimension shows a steep

rise or decline, while correlations between multiple

dimensions illustrate potential relationships in the

data.

C3: Exploration over Creation

Underpinning the design of exploroBOT is the view

that visual analysis is time consuming and potentially

inaccurate when employed by users without training

or expertise. To avoid these difﬁculties, we posit

the idea of exploration over creation so that instead

of creating a small set of visualizations, the user

browses a large repository of visualizations using

simple interactive techniques.

C4: Maintain Context and Sequence Visualizati-

ons

A problem with sequencing visualizations (as op-

posed to a multi-view or coordinated displays) is

maintaining context during analysis (Unwin et al.,

2006). A system should not just drop a user into an

explorative session and continually suggest visualiza-

tions to the user; each visualization requires reorien-

tation as the user tries to understand how the data is

presented.

This is achieved by asking the user how they

would like to change (Section 3) the existing vi-

sualization – different design (design variation),

different data (data variation), more data or less data

(data augmentation or reduction) – we select a new

visualization from the repository and present that

graph to the user. These changes in charts are not

random but aimed at supporting the users exploration.

C5: Enable Iteration and Path Discovery

Our work is founded on the assumption that multi-

ple exploratory paths exist within a dataset and that

chaining together visualizations in a coherent manner

helps to develop an understanding of the data, pro-

ducing insightful analysis. Users should be encoura-

ExploroBOT: Rapid Exploration with Chart Automation

229

Table 1: Task questions and answers.

Questions

1 What is the Female percentage among passengers?

2 How many children aged under 10 y.o?

3 What was the Median Fare of those Embarked as ”C” ?

What was the Age of oldest person Embarked

and their Gender?

Answers

1 (pichart, 32.5%)

2 (histogram, 50)

3 (barchart, 60)

4 (bubble plot, age/Embarked/gender; 70, female)

ged to explore varying paths, while new or alternative

paths for analysis should be suggested.

5 EVALUATION

We conducted a study to compare exploroBOT with a

leading Business Intelligence tool, Tableau. Tableau

is the market leader in exploratory visual analytics

and is promoted as a user friendly and rapid analytics

tool. Tableau’s principal interface is based on a shelf

metaphor – where the users place various dimensions

or measures on a canvas – where the visualizations

are rendered. This approach to visual analytics is a

common metaphor in BI tooling.

Tableau enables users to access all elements of a

chart, along with required settings for a wide variety

of chart descriptors. Thus, it provides a highly versa-

tile tool for designing accurate visualizations. Oppo-

sed to Tableau, the exploroBOT has data exploration

as its main scope with minimal access to chart details.

Therefore, here we compare only the visual data ex-

ploration process – considering the user access time

and results accuracy (how many answers were correct

out of the total) between a complete user-deﬁned chart

design (Tableau) and a basic exploratory tool (explo-

roBOT).

5.1 Study Design

We compared exploroBOT with Tableau on a single

dataset (the Kaggle Titanic dataset

) using a between

group methodology (2 groups, 2 apps, 1 dataset). The

dataset was selected because it is interesting, people

can relate to the information within and it has a rela-

tively equal division of dimensions (categorical) and

measures (numerical) variables.

The tasks were questions (Table 1) on information

that can be extracted using basic visual analytics. We

measured task accuracy and time to solve the task on

www.kaggle.com/c/titanic/data

four questions. Two additional questions were provi-

ded at ﬁrst, to allow the participants begin their ses-

sion and get accustomed to the dataset and interface.

We purposefully did not provide any training on ex-

ploroBOT as the the aim of the tool was to enable

exploration without training or support.

5.2 Participants

We recruited 30 participants from an MSc course in

data visualization. The participants had undertaken

three projects with Tableau – and should have begin-

ner to intermediate familiarity with the tool – but were

unfamiliar with exploroBOT.

This cohort of students were considered suitable

as they have experience with Tableau, but would not

consider themselves expert users, and are studying

data visualization. As a result, they have knowledge

of charts and statistical techniques – such as boxplots

and histograms – as well as simple visualizations,

such as pie charts and bar charts.

5.3 Study Protocol

The study was conducted in two cohorts with each

cohort split between each tool. As mentioned, the

participants were not provided with any training on

exploroBOT but a short presentation of the tool was

provided in a previous class. Each group - Tableau

and exploroBOT - were given the questions, the da-

taset and the tool. They were asked to complete the

question sheet (there was no time limit on the study).

Each question sheet had four multiple choice questi-

ons (four tasks) and the participants were also asked

to mark the time they took to answer each question.

Then they were asked to provide some feedback on

their experience of the tool. The study time averaged

at about 20 minutes.

5.4 Analysis of Results

We hypothesised that visualization exploration (as de-

monstrated with the exploroBOT tool) would result

in more rapid and accurate insights (measured as the

accuracy and time of a task) when compared with

visualization creation (as demonstrated with the Ta-

bleau tool). We measured task time and task accuracy

(as percent of correctly answered questions out of to-

tal number of questions). Their means and conﬁdence

intervals are shown in Table 2.

Given the relatively small number of participants

(15 for each group) the distribution of time and accu-

racy do not meet the requirements of normality, evalu-

ated using the Shapiro-Wilks test (Shapiro and Wilk,

IVAPP 2019 - 10th International Conference on Information Visualization Theory and Applications

230

Table 2: Mean values with 95% conﬁdence intervals.

App

Accuracy

[%]

Time

[min]

exploroBOT 83.3 ± 15.4 10.8 ± 7

Tableau 58.3 ± 22.5 19.4 ± 10

Table 3: Results of Statistical Tests for the Time parameter.

exploroBOT

Tableau p-value

Test p-values range

Shapiro-Wilk 0.006 0.106

<0.05 vs

>0.05

Lavene’s Test 0.145 >0.05

Welch Anova 0.011 <0.05

Kruskal-Wallis 0.005 <0.05

Wilkoxon 0.005 <0.05

1965). Therefore we could not ensure the validity

conditions required of a classic ANOVA test. For both

groups, the results for time and acccuracy have non-

normal distributions (with one exception, the Time

for the Tableau, Table 3) although the variances (eva-

luated using the Levene’s Test) (Levene, 1960) are

homogeneous (Table 3 and Table 4). As a result,

the means were compared using non-parametric tests:

the one-way ANOVA not assuming homogeneous va-

riances, the Welch one-way test (Welch, 1951), the

Kruskal-Wallis test, a median of all pairwise differen-

ces (Salkind, 2007) and the Wilcoxon-Matt-Whitney

test (Neuh

auser, 2011), which does not require nor-

mal distributions.

For the above three tests, the p-value is less than

0.05 (Table 3 and Table 4) suggesting we can reject

the hypothesis H0 of identical populations. For both

criteria (time and accuracy), the test showed a sig-

niﬁcant statistical difference (at 0.05 level) between

groups, suggesting that the group using exploroBOT

completed the tasks more rapidly and more accurately

than the group using Tableau (details in Table 3 and

Table 4). The difference between groups is evident in

the box plots in Figure 6.

Participants feedback reﬂects the results of the

study, commenting that the tool could be ”very use-

ful for exploratory analysis”, that ”exploroBOT was

a good, useful tool” and that ”it was straightforward

to use as the graphs were automated”. Other parti-

cipants suggested that it ”was easy to use and that it

shows you data with a particular combination which

you haven’t thought of, which is good”. However,

participants also found the inability to select required

variables directly as frustrating when asked to com-

plete a goal-directed task. It is important to note that if

the tasks were more explorative this frustration would

be less prominent.

Table 4: Results of Statistical Tests for the Accuracy.

exploroBOT

Tableau p-value

Test p-values range

Shapiro-Wilk 0.0014 0.049 both <0.05

Lavene’s Test 0.2 >0.05

Welch Anova 0.0016 <0.05

Kruskal-Wallis 0.003 <0.05

Wilkoxon 0.003 <0.05

Figure 6: Boxplots of Accuracy and Time test results for

exploroBOT and Tableau.

6 DISCUSSION AND FUTURE

WORK

Compared to VisExemplar and VisRec which recom-

mend visualizations learned from user preferences,

exploroBOT is an exploration system which does not

recommend a speciﬁc chart but enables the user to

ﬁnd (obtain) desired charts quickly and efﬁciently.

Like VizRec, exploroBOT uses data’s statistical pro-

perties to indicate (when required) the interesting

charts but allows the user to see such charts in the

context of an exploratory path, along with easy choi-

ces to ﬁnding other or same type of charts.

Although we showed that exploring visualizations

based on the notion of “interestingness” can result in

rapid and accurate results on a speciﬁc set of tasks

using a speciﬁc dataset, the participants found that

exploroBOT may be frustrating to use. Here, we only

focused on task time and accuracy and did not account

for recall.

There is clearly a beneﬁt to the approach, but furt-

her work is required. Firstly, there is scope to better

understand how feature selection can be improved or

supported beyond ”interestingness”. Possibly, inclu-

ding a reversed “Show Me” feature (in Tableau, sug-

gesting visualizations for selected dimensions) here,

appropriate features can be suggested based on the

selected design, reducing the number of features to

those suitable. Secondly, the act of creating a chart

could beneﬁt from further investigation (new chart ty-

pes, adjustable histograms, graphs, also new visual fe-

atures – colors, transparency, glyph-size adjustment).

ExploroBOT: Rapid Exploration with Chart Automation

231

7 SUMMARY

In this paper, we described exploroBOT, a novel sy-

stem developed to support rapid exploration using a

combination of automatic chart generation and intui-

tive navigation.

expoloroBOT enables quick data exploration

using automatic charts and a user-driven decision path

based on the “browsing exploration metaphor” sup-

ported by a novel visual guidance framework. This

allows a fast retrieval of the sought type of chart with

minimal effort in chart design.

Through an evaluation experiment, we found that

exploroBOT enabled swift and accurate data explo-

ration. Considering these results, we reﬂect on our

approach and suggested several directions for future

work. A video demonstration of exploroBOT is avai-

lable on YouTube: https://youtu.be/C4iPvRvwUEA.

ACKNOWLEDGEMENTS

This work is funded by the Centre for Applied Data

Analytics Research (CeADAR) and co-funded by En-

terprise Ireland (EI) and the International Develop-

ment Agency (IDA) in Ireland.

REFERENCES

Amar, R., Eagan, J., and Stasko, J. (2005). Low-level com-

ponents of analytic activity in information visualiza-

tion. In Proceedings - IEEE Symposium on Informa-

tion Visualization, INFO VIS, pages 111–117.

Bouali, F., Guettala, A., and Venturini, G. (2016). VizAs-

sist: an interactive user assistant for visual data mi-

ning. Visual Computer, 32(11):1447–1463.

Gotz, D., When, Z., Lu, J., Kissa, P., Cao, N., Qian, W. H.,

Liu, S. X., and Zhou, M. X. (2010). HARVEST: An

Intelligent Visual Analytic Tool for the Masses. In

Proceedings of the ﬁrst international workshop on In-

telligent visual interfaces for text analysis, pages 1–4.

Graham, W. and Wilkinson, L. (2010). Autovis: automatic

visualization. Information Visualization, 9(1):47–69.

Kim, Y., Wongsuphasawat, K., Hullman, J., and Heer, J.

(2017). GraphScape : A Model for Automated Reaso-

ning about Visualization Similarity and Sequencing.

In CHI 2017 Conference on Human Factors in Com-

puting Systems, pages 2628–2638, Denver, CO, USA.

ACM.

Levene, H. (1960). Robust tests for equality of variances. In

Ingram Olkin; Harold Hotelling; et, editor, Contribu-

tions to Probability and Statistics: Essays in Honor of

Harold Hotelling, chapter Robust tes, pages 278–292.

Stanford University Press.

Neuh

auser, M. (2011). Wilcoxon-mann-whitney test. In In-

ternational encyclopedia of statistical science, pages

1656–1658. Springer Berlin Heidelberg.

Pak Chung, W., Cowley, W., Foote, H., Jurrus, E., and Tho-

mas, J. (2000). Visualizing sequential patterns for text

mining. In Information Visualization, 2000. InfoVis

2000. IEEE Symposium on, pages 105–111. IEEE.

Perry, D., Howe, B., Key, A. M. F., and Aragon, C. (2013).

VizDeck: Streamlining Exploratory Visual Analytics

of Scientiﬁc Data. In iConference 2013, pages 338–

350, Washington DC, USA.

Saket, B., Kim, H., Brown, E. T., and Endert, A. (2017).

Visualization by Demonstration: An Interaction Para-

digm for Visual Data Exploration. IEEE Transactions

on Visualization and Computer Graphics, 23(1):331–

340.

Salkind, N. J. (2007). Kruskal-Wallis Test. In Encyclopedia

of measurement and statistics. Thousand Oaks, CA:

SAGE Publications, vol.1 edition.

Scheibel, W., Trapp, M., and D

ollner, J. (2016). Interactive

Revision Exploration using Small Multiples of Soft-

ware Maps. In 11th Joint Conference on Computer

Vision, Imaging and Computer Graphics Theory and

Applications IVAPP, volume 2, pages 131–138.

Shapiro, S. S. and Wilk, M. B. (1965). An analysis of va-

riance test for normality (complete samples). Biome-

trika, 52(3/4):591–611.

Shneiderman, B. (1996). The Eyes Have It : A Task by

Data Type Taxonomy for Information Visualizations.

In Visual Languages, 1996. Proceedings., IEEE Sym-

posium on, pages 336–343.

Stolper, C. D., Perer, A., and Gotz, D. (2014). Progres-

sive visual analytics: User-driven visual exploration

of in-progress analytics. IEEE Transactions on Visua-

lization and Computer Graphics, 20(12):1653–1662.

Unwin, A., Theus, M., and Hofmann, H. (2006). Graphics

of large datasets: visualizing a million. Springer

Science & Business Media.

Vartak, M., Huang, S., Siddiqui, T., Madden, S., and Para-

meswaran, A. (2015a). Towards Visualization Recom-

mendation Systems. In Workshop on Data Systems for

Interactive Analytics (DSIA).

Vartak, M., Madden, S., Parameswaran, A., and Polyzotis,

N. (2015b). SEEDB : Supporting Visual Analytics

with Data-Driven Recommendations. In VLDB.

Welch, B. L. (1951). On the Comparison of Several

Mean Values: An Alternative Approach. Biometrika,

38:330–336.

Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay,

J., Howe, B., and Heer, J. (2016). Voyager: Explora-

tory Analysis via Faceted Browsing of Visualization

Recommendations. In IEEE Transactions on Visuali-

zation and Computer Graphics, volume 1, pages 1–1.

IEEE.

Yalc¸n, M. A., Park, C., and Bederson, B. B. (2016). Cogni-

tive Stages in Visual Data Exploration. In BELIV ’16,

October 24 2016, Baltimore, MD, USA. ACM.

IVAPP 2019 - 10th International Conference on Information Visualization Theory and Applications

232