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Challenges of Big Data Visualization in Internet-of-

Things Environments

Doaa Mohey Eldin

Information Systems Department

Faculty of Computers and Information

Cairo, Egypt

d.mohey@alumni.fci-cu.edu.eg

Aboul Ella Hassanien

Information Technology Department

Faculty of Computers and Information

Cairo, Egypt

aboitcairo@gmail.com

Ehab Ezat

Information Systems Department

Faculty of computers and Information

Cairo, Egypt

E.Ezat@fci-cu.edu.eg

Abstract— big data infers in all processes in our life. But the

analysis process of huge data is not sufficient, the human brain

heads for detecting a pattern more efficiently when data is

performed visually. Data Visualization and Data Analytics play

a significant role in decision making in various sectors. It also

opens new opportunities for the ideas for big data visualization

domain. An essential challenge in data visualization is a huge

amount of data in real time or in static form. This paper

discusses the importance of data visualization. This paper

illustrates the impact of big data on data visualization. It

focuses on the explanation of the essential challenges of big

data visualization in the real-time stream.

Keywords— Big data, Data visualization, challenges,

Internet-of-Things, Interactive visualization.

I. INTRODUCTION (HEADING 1)

The rate of growth of data has increased exponentially

within few years due to several factors like Internet of

Things (IoTs), and sensors. Internet of things (IoT) [1]

becomes one of newly important concepts of computing that

describes the motif of smart objects [2] being connected via

the internet that can simulate the physical world and objects.

It provides the pattern recognition and devices identification

methods called by sensors. These sensors have a big data [3]

in different volume, variety types and velocity transmission.

So the extracted data from these sensors requires to manage,

fusion, and visualize them as shown in figure.1.

Figure.1: The Basic Architecture of the Internet-of-Things Sensors Big

Data Processes.

So the visualization of big data becomes a hot area of

research recently. These sensors provide various domains

such as mange the patient data remotely in telemedicine [4],

fusion [5] the student’s data profiling in video conferences

[6] and E-learning, and observation the traffic flow [7]

continuously. These data needs to save in every seconds that

requires a huge storage for the massive amount of this data.

These data can’t show for the user as doctor, professor, or

police officer in tables and row data, but these data requires

to analyze, interpret and present in meaningful ways. The

main challenge of Big Data [8] lies in capturing, storing,

analyzing, sharing, searching, and visualizing data. So the

visualization of these data is very important process to

control any emergency cases or observe the evolution of

students, patients, streets and so on. One of the major

aspect of Big Data analysis is that we can find interesting

pattern in huge data set, but actually the result of the

analysis is usually raw numbers and by those numbers it is

very hard to explicate anything. But if those numbers are

represented visually then it becomes much easier for our

brain to find meaningful patterns and take decision

accordingly.

Data visualization [9] is certainly not a new thing; it has

been around for centuries. Data visualization is an easy and

quick way to convey messages and represent complex

things. Humans are adapted to find patterns in everything

we see. Since the data is mounting at such a massive rate the

traditional ways of presenting data is obsolete. Compared to

traditional data.

This paper discusses the data visualization concept and

the main architecture of it. This research provides the

importance of data Visualization and the vital role in

decision making. It presents the essential challenges of data

visualization and its relationship with big data. It can

support the researchers in selecting a topic from open

research directions of data visualization.

The rest of this paper is organized as follows: Section

2 presents the state of art. Section 3, benefits of the data

visualization. Section 4, the presentation of challenges of

data visualization and recent researches and tools work.

Finally, Section 5 conclusion and proposes directions for

future work.

II. THE STATE-OF-ART

This section discusses the relationship between big

data and data visualization. The data visualization [9, 10, &

11] targets to implement a tool for visualize the data. This

tool has relies on the statistical models and several attributes

such as color, distribution, graph style [12] (Pie-chart, bar,

or etc.). Implementing effective data visualization solutions

for Big Data has to take into account apart the volume of the

data, and other intrinsic constraints generated by the typical

characteristics of Big Data [13]: real-time changes, extreme

variety of the sources (Multi-source integration), and

Sensors Data management (IoT

network)

Big data fusion and analytics

Data

Visulization

different levels of data structuring. Furthermore, the

recommendation of synchronous techniques of the data

visualization usage to preferable illustrate relationships

among a large amount of data. The techniques enhance

decisions improve the Data in motion: that refers to analysis

of streaming data to enable decisions within fractions of a

second. The Data at scale: shows the Petabyte (1015

) to

Exabyte (1018

). Data in several formats relies on the

structured, unstructured, Text, multimedia. Complex

information Spaces: that depends on the data items being

difficult to compare based on raw data, data compound of

several base data types. Three critical elements in applying

visual analytics to extreme-scale data and complex

Information Spaces: Size, Inclusion of visual and analytical,

and Active involvement of a human. The Complexity and

flatness: the world is complex, dynamic, and Multi-

dimensional.

Figure.2: The Relationships between Big Data and Data Visualization

Characteristics

Big data has several characteristics validity, variety,

volume and velocity. They refers to the size of data in

different types. And the speed of changes theses data relies

on the velocity. The availability refers to the data is

available along time. The visualization of data is

considered one characteristics of big data because the

enormous number of data requires to summarize into

valuable graphs to support the users by results easily. Data

visualization faces several characteristics of big data such as

context-aware properties that depends on the type of inputs

and analytics data (video, audio, image, or text). This

property requires to treat the various topologies and convert

them into one topology structure to interpret the data

accurately. Data visualization is very sensitive [14] to users

of targeted data fusion. That meaning the visualization

usually relies on the specific domain. It also can deal with

changes simultaneous with variant attributes.

The target of data visualization is expressed by the

visual representation of rich sensors data in Internet-of-

things. This representation requires several measurements

techniques for simulate the physical world. The induction of

this relationship the data visualization relies on the types

and motions of big data.

Figure.3: The Data visualization lifecycle based on the Big

Data.

Data visualization has an effective role in visualizing

the valuable data and detecting the outlier [15] from the big

data. This vital role faces essential variant processes that,

mentioned in figure.3, goals and users identification, the

cleaning, integration, fusion, and analytics.

The data visualization has our layers in processing:

Figure.4: The Data Visualization Layers

The results of Visualization expresses the abstract

data, useful, clear, and synthetic data. These automatic

results of analysis requires to summarize graphically. The

good graph which had valuable and meaningful data, so the

selective graph or report type and color is very important for

users.

III. DATA VISUALIZATION METHODOLOGIES

There are Three Styles of Big Data Visualization as

shown in Table.1: data reduction [16], visual interaction

[17], and High Performance Computing (HCP) [18] .

Data Visualization

Big Data

Big Data

Goals idenfitication

Users Detections

Data Cleaning Data

Fusion/Intgeration

Data Analytics

Data Visualization

Dat

a vi

sual

izat

ion

laye

rs preprocessing Data

identify the suitable graphs

Results Analytics

Outliers Detection

Figure.5: Data Visualization Methodologies of big data

The first type of methodologies has several

motivations. It refers to the data reduction methodology.

Data reduction [16, 17] refers to the process of numerical or

text transformation into digitalized data ordered, accurate,

and simplified format. (Wickham) refers to the big data

filtering and feature reduction, [17,18] proposed a

visualization tool for molecular biological that can study the

behavior of the system based on data-driven. It depended on

the probabilistic latent variable model to produce data and

measures. There exist lots of graph drawing algorithms,

including string analogy-based methods. Most of them focus

explicitly or implicitly on local properties of graphs,

drawing nodes linked by an edge close together but avoiding

overlap.

The second type refers to the Interactive Visual

Analysis (IVA) [19] that relies on a group of techniques for

integrating the computational power of systems with the

aware and cognitive strengths of humans. In order, to extract

knowledge from large and complex datasets. The techniques

rely heavily on user interaction and the human visual

system, and exist in the intersection between visual

analytics and big data. It is a branch of data visualization.

IVA is an appropriate technique to the data analytics with

high-dimensions [20, 21]. Star Glyphs [22] and parallel

coordinates interaction Visual interaction that refers to the

representation of visual interaction [21] .

High-performance computing (HPC) [18] is the third

type of methodologies for data visualization that is

considered the use of super computers and parallel

processing techniques for solving complex computational

problems. HPC technology focuses on developing parallel

processing [22] algorithms and systems by incorporating

both administration and parallel computational techniques.

It relies on dividing and conquer methodology and depends

on the parallel computation [23].

Recently, the motivations of research goes to use machine

learning [24, 25] and deep learning algorithms [26][27] to

understand more features and reduction of data analysis to

reach the highest accuracy and performance.

IV. DATA VISUALIZATION BENEFITS

Data visualization is viewed by many disciplines as a

modern equivalent of visual communication. It is viewed as

a branch of descriptive statistics. Main objective of data

visualization illustrates in sharing and communicating

knowledge and information clearly and effectively through

graphical means. Visualization applications often utilize

visualization libraries; further, they provide an interface that

allows users to combine modules and set their attributes to

produce the desired results. It is highly extensible. New

modules can be developed without modifying the core

infrastructure. The design allows for dynamic composition

of modules. As a result, visualization libraries tend to have

modules that perform small, indivisible tasks that users can

build up to produce the exact visualization they want. The

advantages of data visualization [28, 29] as shown in

figure.6:

Figure.6: The Data Visualization Advantages

According to figure.6, a critical element in reducing the time

amount needed to understand data, visualization

applications are insistent realizing the value from a Big Data

initiative, to minimize the time required to know where

opportunities, issues, and risks reside in voluminous data.”

The gathering between data analytics, visualization

that should to be meaningful, valuable and experienced data

in rapidly manner to support users’ objectives and decisions.

The core of data visualization relies on the good analytics

and anomalies detection to enhance business or individual

targets.

V. DATA VISUALIZATION CHALLENGES

Big Data may be challenged to yield significant

actionable results. According to figure.7, the challenges can

be summarized into six challenges the lack of expertise in

learning data. Data visualization mostly works on specific

domain so that needs to the expert to manage the results.

The statistical results refers to the meaningful to take a

decision. If the system has several users that is very heavy

to be the data available to all users concurrently to get the

results of the processes as searching, getting reports and so

on. The data should to have analytics to reach of the

meaning of data not meaningless numbers. The helpful

analytics which can infer in the decision making, such as if

the patient has low or high pressure, what he can do. What

Data Visualization Methodologies

Dimesnsion Reduction

Visual Interaction High performance

computing

TimeSaving

provide self-service

s foruser

improve

collabortion

betterbig

dataanalysi

s

improve

decision

making

Series 1 20% 34% 41% 43% 77%

20%

34% 41% 43%

77%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

per

ecn

tage

rat

e

benefits of data visualization

will happen in the emergency cases? That requires to

contact to doctor or communicate with the nearest hospital

or ambulance to save his life. May be the results are similar

but the meaning of them not the same so the difficulty of

understanding. Also the users requires to have the correct

numbers in every point with right interpretation of these

data.

Running the queries, processes and reports on these

data takes a long time that is also a challenge of the data

visualization. The classical tools for data visualization are

limited with the big data can’t interpret the huge data and

changes continuously. They worked to improve latency of

data but they are still faced performance problems. For any

Big Data visualization tool should to be able to deal with

semi-structured and unstructured data because big data

usually have variant format data types. The parallelism

approach is not an enough solution because it featured to

break down frequently.

The challenges [13, 29, 30] of big data visualization

are not focus on the industry of business only but also

targets the researchers to improve the results and tools. So

this paper shows open research problems of visualization to

support researchers in finding a new open research areas and

directions.

Figure.7: the data visualization challenges

These challenges faces data visualization in the

implementation process of any visualization tool:

Figure.8: the open research challenges of data visualization

A. Context-awareness Data Model

One significant challenge [30, 31] to data models in

existing visualization software is that the types of data they

are expected to handle is growing. In the scientific space,

new refinement structures, new types of polynomial fields,

and even high dimensional grids are becoming more

common. The need for these structures are motivated both

by the demands of new science and by the evolution of

scientific computing algorithms.

Internet-of-things environments and mobile sensors

environments are considered a hot area of research in data

visualization that requires to unite the structure of the

variety of context into one context-type. The researchers

[19, 20] proposed a system to generate interactive context-

aware visualization of federated data sources provided by an

underlying context-aware framework called Augmented

World Model (AWM). It relies on the extracted data from

internet-of-things devices and sensors in real-time.

The authors presented a new visualization tool entitled

[31] to visualize big data graphically and detect the outliers

of data such as error or event based on historical tutorial.

Recently, this research [32] talked about Rules in Mobile

Context-Aware Systems and provides a SKE to the

development of mobile context-aware systems. The problem

of this research in the output data extracted from Mobile

sensors in performance and support the correct meaning of

users, the adaptive with users, conditions, environments,

efficiency with high usage resources and responsiveness.

This research presented [33] a new paradigm of the

data visualization based on machine learning of context-

aware identification. It provided the recommendations of the

charts types for given datasets based on specific domain.

The authors [34] proposed a systems for data visualization

that could support the integration of common visualization

modules in big data streams. It was based on the parallelized

visualization that can be scalable and flexible for the

heterogeneous data.

The previous observation of several visualization tools

that can manage the context-aware types, we notice that: the

visualization tool requires to be:

1. Adaptive: dynamic system

2. Easy to use: to allow users to change, update and

search by various dimensions such as time,

geographic locations.

3. High performance (low running time)

4. Classify patterns: that is based on features,

conditions, and patterns recognition.

Other important notion there is a relationship between

the fusion level of data in IoT or integration process and the

reliable results of visualization tool.

, (1) or

(2)

The equations 1, 2 refer to the correct fusion or

integration produces the reliable visualization results. On

other words, the high fusion causes of the high reliability of

visualization. Another observation of the high detection of

takes

a

long

time

diffic

ult to

share

diffic

ult to

analy

ze

not

effiec

tive

data

too

diffic

ult to

acces

s all

data

lack

of

exper

tise

precentage rate 19% 22% 37% 45% 50% 57%

19% 22%

37%

45%

50%

57%

0%

10%

20%

30%

40%

50%

60%

Pre

cen

tage

rate

Challenges of Data Visualization

outliers can support the results of visualization accuracy as

equation 3.

, (3)

B. Transparency Visualization

It can Support the user in understanding the reasons

behind the recommendations. There is still challenge

concept because that there is lack of explanation of why are

the results showed. Recommender systems [18] lack

transparency, when they appear as "black boxes" to the user,

making it incomprehensible how recommendations are

generated and why a specific list of items is presented.

Improving transparency of data visualization is requires to

avoid risk management process in business. That also

provides any user to recognize the reasoning behind the

visualization results.

The proposed method used to enhance visualization

transparency (as known by "Justification") of a

recommender system and the users’ trust in the results, are

explanations. They can help users to understand the reason

behind a recommendation, increase the user’s sense of

involvement in the recommendation process and can lead to

a greater acceptance of the recommender system as a

decision aide. The concept affords transparency of the

recommendations by visualizing an average rating (position)

as well as an individual rating for each user (glyphs). Viola

[31] also provided reasoning and explanation of the outliers

in data visualization.

This is still open research in visualization, the recent

motivations targets using deep learning and data reduction

based on features for creating an explanation of the data

visually.

, (4)

(5)

Our observation shows a high relationship between the

correct data reduction has Positive relationship with correct

fusion that will effect on the reliable visualization as

equations 4, and 5.

C. Social Internet-of-things (SIoT)

The internet-of-things refers to interconnected set of

sensors connected via internet that hold huge data about one

or more objective. The social internet-of-things [ 35, 36]

refers to the various user’s targets and the affected

attributes. There are levels of meaning of any data that are

very sensitive with the user authorities and goals. The

Interactivity of visualization data is complex process

because it is based on the complex features and levels of

data. So that will require new motivations to can manage the

data levels with user’s variety. The observation here reach to

this relationships in equation 6 and 7.

, (6)

(7)

D. Virtual Reality (VR):

Virtual Reality is going to have a huge impact on the

potential for data visualizations [18], allowing people to

interact with data in the third dimension for the first time.

Imagine being able to pick a data set and move it around on

any axis to compare it to another, it isn’t too far away.

According to SAS. People can process only 1 kilobit of

information per second on a flat screen, which can be

increased significantly if it’s analyzed in a 3D VR world.

This challenge has a big effect on the future results, such as

profit of the finance data. The visualization tools can predict

the profit, and loss in several cases and graph the

imagination based on real data concurrently. That can

improve the performance rate yearly.

VI. CONCULSION AND FUTURE WORKS

This paper presents a survey study of data visualization

as a hot area of research. It illustrates the relationship

between the big data and data visualization. It demonstrates

the benefits of the visualization. This paper aims to explain

the four challenges in this field: Context-awareness,

transparency, social internet-of-things based on the different

levels of understanding and virtual reality.

Challenges are discussed in the following briefly,

A. Context-Awareness: Considering different

situations, such as mood, time, individual, or

collaborative scenarios

B. Transparency. Supporting the user in understanding

the reasons behind the recommendations.

C. Social Internet-of-Things and Meaningful Data

levels: The difficulty for designing visualizations to

match up to the wide-ranging understanding of data

and data visualizations.

D. Virtual Reality: is going to have a huge impact on

the potential for data visualizations, allowing

people to interact with data in the third dimension

for the first running time.

In future work, we will propose a new visualization tool

can treat the mentioned challenges to support social internet-

of-things. This tool should to be adaptive, flexible, and easy

to use. That can will improve the fusion and integration

problems to support the high accuracy and performance of

the context types. We target to optimize the time

automatically to work on the real-time streams analysis.

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