Big Data Visualization Tools: A Survey

The New Paradigms, Methodologies and Tools for Large Data Sets Visualization

Enrico G. Caldarola

1,2

and Antonio M. Rinaldi

1,3

Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Napoli, Italy

Institute of Industrial Technologies and Automation, National Research Council, Bari, Italy

IKNOS-LAB Intelligent and Knowledge Systems, University of Naples Federico II, 80134 via Toledo, 402, Napoli, Italy

Keywords:

Big Data, Big Data Visualization, Graph Visualization, Information Visualization, Big Data Analytics, Visual

Analytics.

Abstract:

In the era of Big Data, a great attention deserves the visualization of large data sets. Among the main phases

of the data management’s life cycle, i.e., storage, analytics and visualization, the last one is the most strategic

since it is close to the human perspective. The huge mine of data becomes a gold mine only if tricky and

wise analytics algorithms are executed over the data deluge and, at the same time, the analytic process results

are visualized in an effective, efﬁcient and why not impressive way. Not surprisingly, a plethora of tools and

techniques have emerged in the last years for Big Data visualization, both as part of Data Management Systems

or as software or plugins speciﬁcally devoted to the data visualization. Starting from these considerations, this

paper provides a survey of the most used and spread visualization tools and techniques for large data sets,

eventually presenting a synoptic of the main functional and non-functional characteristics of the surveyed

tools.

1 INTRODUCTION

The rate of data growth over years is amazing: ac-

cording to ScienceDaily, a full 90% of all the data in

the world has been generated over the last two years

(Dragland, 2013). All of this represents a real tsunami

and requires a paradigmatic shift respect to the past as

for theories, technologies or approaches in data man-

agement and more attention to survive it (Caldarola

et al., 2014). In order to effectively spot, at the same

time, this explosion of data and the spreading inno-

vative technological solutions able to cope with this

huge volume of data, a new term has been coined,

i.e., Big Data, which is receiving a lot of buzz in the

recent years (Franks, 2012). In fact, a look at Google

Trends shows that, starting from 2011 until today, the

term Big Data has been increasingly growing in pop-

ularity over time (Weinberg et al., 2013). Depending

on the different perspectives from which the problem

of managing large data sets is seen, we can deﬁne Big

Data in several ways. From a technological perspec-

tive, Big Data represents “data sets whose size is be-

yond the ability of typical database software tools to

capture, store, manage and analyse” (Manyika et al.,

2011). It may also refers to “data which exceeds the

reach of commonly used hardware environments and

software tools to capture, manage, and process it

within a tolerable elapsed time for its user” (Merv,

2011). ¿From a marketers point of view, Big Data

is an organizational and decision problem rather than

a technology problem (Weinberg et al., 2013). Fi-

nally, from a user point of view, Big Data can be

understood as new exciting, advanced software tools

which replace the existing ones. Perspectives aside,

the authors deﬁne Big Data as a new time-variant

paradigm in data management whose raison d’

etre

comes from the enormous availability of data in ev-

ery human activity that needs to be acknowledged

according to different points of view: technologi-

cal, economical, scientiﬁc and so on. With the ad-

vent of the Big Data paradigm, data scientists do not

need to construct a complex model nor to describe

all its rules through complex logic-based languages,

but, they only need to properly tune statistical analysis

or machine learning techniques over large corpus of

data in order to get more insights from them, and very

quickly. Recently, this new approach in taming the

giant wave of available data is tempting several orga-

nizations and individuals due to its real effectiveness

in knowledge discovery. By knowing people’s prefer-

296

Caldarola, E. and Rinaldi, A.

Big Data Visualization Tools: A Survey - The New Paradigms, Methodologies and Tools for Large Data Sets Visualization.

DOI: 10.5220/0006484102960305

In Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017), pages 296-305

ISBN: 978-989-758-255-4

ences and opinions, for example, modern enterprises

may gain a competitive advantage over competitors,

while analysing sensor data from the workshop may

helps manufacturers to improve their processes and

their performances thus reducing costs and increasing

revenue. A study by the Economic Times suggests

that large organizations using Big Data analytics out-

perform competitors, who do not utilize this (Bhanu,

2013). The enthusiasm for the Big Data technolo-

gies and the interest of entrepreneurs or researches for

such solutions has concerned the complete paramount

of tools and frameworks used in the different phases

of data life cycle: from the storage to their analysis,

cleaning or integration and, of course, their visual-

ization. About this latter, in particular, Data and In-

formation Visualization is becoming strategic for the

exploration and explanation of large data sets due to

the great impact that data have from a human per-

spective. In fact, the visualization is the closer phase

to the users within the data life cycle’s phases, thus,

an effective, efﬁcient and impressive representation of

the analyzed data may result as important as the an-

alytic process itself. Not by chance, many visualiza-

tion tools available in the literature, are actually an-

alytics tools whose visualization component become

increasingly important. The challenges that the Big

Data imperative (Caldarola et al., 2015a) imposes to

data management severely impact on data visualiza-

tion. The “bigness” of large data sets and their com-

plexity in term of heterogeneity contribute to compli-

cate the representation of data (Caldarola et al., 2016;

Caldarola et al., 2015b), making the drawing algo-

rithms quite complex: just to make an example, let

us consider the popular social network Facebook, in

which the nodes represent people and the links rep-

resent interpersonal connections; we note that nodes

may be accompanied by information such as age, gen-

der, and identity, and links may also have different

types, such as colleague relationships, classmate re-

lationships, and family relationships. The effective

representation of all the information at the same time

is really challenging. The availability of large data

coming from human activities, exploration and exper-

iments, together with the investigationsof new and ef-

ﬁciently ways of visualizing them, open new perspec-

tives from which to view the world we live in and to

make business. The Infographics become Infonomic,

a composite term between the term Information and

Economics that wield information as a real asset, a

real opportunity to make business and to discover the

world (Caldarola and Rinaldi, 2016).

Taking into account the live interest for Big Data

analytics and visualization tools from entrepreneurs

and researches, this work goes in the direction of

Figure 1: The Big Data Dimensions.

helping the latter in ﬁnding the right tool to use for vi-

sualizing large data sets, by characterizing at a general

level the Big Data problem and its technological chal-

lenges and, then, by surveying the most popular and

spread Big Data visualization and visual analytics so-

lutions existing in the literature. This work originates

from a similar work by the authors (Caldarola and Ri-

naldi, 2015), in which a framework from the qualita-

tive analysis of Big Data solutions was proposed. In

that case, software solutions to store Big Data were

surveyed, while here a similar approach is used for

data and information visualization tools.

The remainder of this paper is structured as fol-

lows. The second section presents the typical model

characterizing the dimensions of Big Data and the

technological solution with a focus on the visualiza-

tion issue. The third section introduces the evalua-

tion framework adopted for the comparison of the Big

Data visualization solutions, whereas the fourth sec-

tion illustrates the results of the comparison carried

out on the most widespread existing tools, based on

the predeﬁned criteria. Finally, the last section draws

the conclusions, summarizing the major ﬁndings, and

opens new directions for further researches in future

works.

2 BIG DATA DIMENSIONS AND

THE DATA VISUALIZATION

The concept of Big Data has different dimensions

since the term Big does not refer only to the quantity

of data but also to the heterogeneity of data sources

and to the velocity in analyzing data. A widely spread

model to characterize Big Data is that of the 3Vs (Mo-

hanty et al., 2013; Jagadish et al., 2014), depicted

in Figure 1, which shows the three fundamental di-

mensions of Big Data: Volume, Velocity and Variety.

Big Data Visualization Tools: A Survey - The New Paradigms, Methodologies and Tools for Large Data Sets Visualization

297

Each of the above dimensions make traditional opera-

tions in data management more complicated. For ex-

ample, if the volume increases, the extraction and the

storage of data becomes challenging as well as data

processing (cleansing, analysis, etc.). Both storage

systems and analytics algorithms must be scalable in

order to cope with the augmented volume of data in

common scenario. In addition, the variety dimension

complicates data storage and analysis because of the

integration of data with different structures. Figure 2

focuses on two operations in data life cycle, namely,

the analysis and the visualization of data, and tries

to sketches the different types of software solutions

existing in the literature (together with some exam-

ples), along the volume and variety dimensions. The

velocity is not taken into consideration here because

is out of the scope of this work and it is likely not

to affect signiﬁcantly the visualization task. The ﬁg-

ure also provides some sub-concepts and paradigms

in Data and Information Visualization that deserves

some explanations. If the main goal of Data Visu-

alization is to communicate information clearly and

efﬁciently to users, involving the creation and study

of the visual representation of data – i.e., “informa-

tion that has been abstracted in some schematic form,

including attributes or variables for the units of infor-

mation” (Friendly and Denis, 2001) – the Information

Visualization main task is the study of (interactive)

visual representations of abstract data to reinforce hu-

man cognition. The abstract data may include both

numerical and non-numerical data, such as text and

geographic information. Additionally, the ﬁgure in-

troduces Scientiﬁc Visualization. According to (Mun-

zner, 2008), it is possible to distinguish Information

Visualization (InfoVis), when the spatial representa-

tion is chosen, from Scientiﬁc Visualization (SciVis)

when the spatial representation is given due to the in-

trinsic spatial layout of data (e.g., a ﬂow simulation in

3D space, the navigation tools provided by the Sloan

Digital Sky Surveys project described later (Eisen-

stein et al., 2011)). Coming back to the Figure 2, it is

possible to distinguish two areas corresponding to the

semi-planes of analytics tools and that of visualization

tools. Each semi-plane is divided into two regions in

turn according to the emphasis given to the dimension

of volume or variety of data, this way having four re-

gions corresponding respectively to big data tools for

the analysis of large data set, the business intelligent

tools for data mining of heterogeneous data, data visu-

alization tools used for visualization exploration and,

ﬁnally, information visualization tools used for the in-

teractive visual exploration of data. Undoubtedly, all

the regions have overlapping zones, particularly the

analytics and visualization areas along both the vol-

ume and the variety axes. This is not surprisingly,

due to the widespread tools that combine the analyt-

ics functionalities with visualization capabilities. In

this regard, along the volume dimension it is possi-

ble to cite, on the one hand software environments

for numerical and statistical computing and graphics

(e.g., Matlab, R, etc.), mostly devoted to analysis and

data processing tasks, and, on the other hand, data vi-

sualization tools equipped with connectors to main-

stream computing environments (e.g., RNeo4J, Stat-

net, etc.). With respect to the variety axis, to the over-

lapping zone depicted in ﬁgure 2 belong all such Busi-

ness Intelligent tools, which aim at adopting sophisti-

cated pictorial solutions in order to reinforce the hu-

man cognition and make analytics via graphical wid-

gets (Visual analytics). With the depicted schema in

mind, in the following section technical and non tech-

nical criteria used for comparing the surveyed tools,

one in its own category, have been individuated and

further discussed.

3 THE SURVEY OF THE

ANALYSED SOLUTIONS

Having described the main categories characterizing

the existing tools for large data visualization, this sec-

tion reports a survey of the most spread solutions with

a description of the evaluation criteria listed as follow,

each of them trying to answer to a speciﬁc question:

1. Scope. It informs about the usage or the scope of

the tool, in other words it tries to answer the fol-

lowing question: Is the tool for presentation or de-

veloper (or both). Among the surveyed solutions

we distinguish two main categories, i.e., presen-

tation tools, mostly used for presentation purpose

through desktop (stand-alone) or web-based ap-

plication, and development tools, mostly software

libraries (APIs) or programming language mod-

ules (e.g., Python or Java module), mostly used

for extending an application by using methods and

routines provided ad hoc by the library;

2. Software Category. It represents the typology of

the analysed solution. It distinguishes between

desktop application (stand-alone app without any

mechanism for extending it), Web-based applica-

tion or services, software library (e.g., Javascript

library for the web), software framework (i.e.,

a complex software application with a plugin or

add-ons based mechanism to extend it in order

to connect the framework to existing solution for

data storage or analytics);

3. Visualization structure. It answers the question:

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Figure 2: The Big Data Visualization Solutions.

What kind of graphical object the tool is cut out

for? Thus, it informs about the main graphical

object or widget supported by the tool. Many so-

lutions are not limited to a single graphical object

but generally, there is one or more the one widgets

the tool is cut off. For example, Plottly is cut off

for chart widgets, while Polymaps for maps;

4. Operating System, O.S. The operating system

(e.g., Linux, Windows, Mac OS X) on which the

tool runs, or if it is web-based;

5. License. This informs about the license of the so-

lution: commercial and open source under various

licenses (Apache License, GNU GPL, etc.);

6. Scalability. This criterion is about the mecha-

nisms for horizontally scaling the tools in order to

support very large data set. Some of the solutions

concern, for example, the possibility to connect

the software to an Hadoop instance running over

a large dataset or using the cloud according to the

pay-as-you formula;

7. Extendibility. This criterion is about the mecha-

nisms for extending the tool throughout add-ons

or plugins mechanism, and also the possibility to

connect it to existing storage solution. For ex-

ample, Plottly can be connected to Matlab, R, by

means of speciﬁc client connectors, while by us-

ing the Javascript APIs, it can be programmati-

cally extended in different ways;

8. Latest Release version and Date. It tries to ﬁgure

out if the solution is up to dated or not. If the

latest release date is not recent, the product may

no longer be supported.

All the information above have been extracted

from the ofﬁcial websites of each solution surveyed, if

immediately available from the website sections, oth-

erwise they are omitted. Table 1 reports the evalua-

tion criteria for 36 software tools for data visualiza-

tion, each of them has been brieﬂy described in the

next sub section.

3.1 The Surveyed Tools

The software tools collected in this survey have

been grouped into four subsections according to the

schema depicted in ﬁgure 2 and described as follows.

3.1.1 Information Visualization Tools

To the South East region of ﬁgure 2 belong interac-

tive visualization tools and dashboards together with

Big Data Visualization Tools: A Survey - The New Paradigms, Methodologies and Tools for Large Data Sets Visualization

299

charts-oriented libraries or APIs, categorized as Infor-

mation Visualization tools or modules. In the follow-

ing paragraphs the surveyed tools belonging to this

category are brieﬂy outlined.

Tableau

is the big data visualization tool for cor-

porate, which allows to create charts, graphs, maps

and many other graphics. It is cut off for charts and

is based on a desktop application available for visual

analytics. Along with the Desktop edition, a server

solution lets the user to visualize reports online and

on mobile app. In this case a cloud hosted service is

also an option, which allows the customer to install

the solution on premises.

Infogram

offers several interactive charts and

numerous maps to help user in visualizing data in a

pleasant way. The tools is cut off for charts objects

including column, bar, pie, or word cloud. It belongs

to Infographic software category since the user can

even add a map to her infographic creating impres-

sive report. Infogram supports team accounts for me-

dia publishers and for journalists, branded designs for

companies and classroom accounts for educational

projects.

ChartBlocks

ChartBlocks is an online tool that

requires no coding, and builds visualizations from

spreadsheets, databases and live feeds. Chart are

created under the hood in HTML5 by using the

JavaScript library D3.js. Being web-based this tools

creates charts and widgets compatible with any screen

size and device. It is also possible to embed charts in

any web page and share it on Twitter and Facebook.

To this category also belong libraries or modules

for creating chart or graphical widgets mostly inside

web applications using Javascript objects and func-

tions such as:

Plottly

, which is a tool for creating sharp and

slick chart starting from a simple spreadsheet. Var-

ious important comapanies use Plottly, for example,

Google and also the U.S. Air Force, Goji and The

New York University. Plottly is mainly a web tool

but offers an API for different languages that include

JavaScript and Python.

D3.js

is a JavaScript library for manipulating

documents based on data. D3 helps users to encapsu-

late data in HTML, SVG, and CSS ﬁles. D3s empha-

sis on web standards gives the user the full capabili-

ties of modern browsers without using a proprietary

framework, combining powerful visualization com-

ponents and a data-driven approach to DOM manipu-

https://www.tableau.com/products

https://infogr.am/

http://www.chartblocks.com/en/

https://plot.ly/

https://d3js.org/

lation.

Ember Charts

is based on the Ember.js frame-

work and uses D3.js under the hood. Ember Charts

features time series, bar, pie and scatter charts. It can

be easily extended, provides best practices and inter-

activity and is robust when fed bad data.

Google charts

Google Charts is a Java library

running on HTML5 and SVG and aims at Android,

iOS and total cross-browser compatibility, including

older Internet Explorer versions supported via VML.

All the charts created are interactive and some are

even zoomable. Google Charts is very user friendly

and their site features a really nice and comprehensive

gallery where users can see the kind of visualizations

and interactions they need.

FusionCharts

the most comprehensive

JavaScript charting library, and includes over 90

charts and 900 maps. FusionCharts integrates easily

with libraries like jQuery, frameworks like AngularJS

and React, and languages like ASP.NET and PHP.

FusionCharts supports JSON and XML data, and is

able to export charts in a multitude of formats: PNG,

JPEG, SVG and PDF.

chart.js

, is an open source, tiny, fast, easy to use,

library supporting six chart types: doughnut, pie, po-

lar, line, bar and radar. Chart.js uses HTML5 Canvas

and ships with polyﬁlls for IE6/7 support.

Leaﬂet

, is an open-source and light-weight

Javascript library cut off for maps, which leverages

OpenStreetMap data and adds HTML5/CSS3 visu-

alizations and interactivity on top to ensure every-

thing is responsive and mobile ready. It is possible to

use their extensive plugin repository to add heatmaps,

masks and animated markers.

Chartist.js

is a JavaScript charting libraries,

which leverages Sass and styles customizable Chartist

provides a separation of concerns between CSS styles

and JavaScript functions, and its SVG output is re-

sponsive, media query based and DPI independent.

Chartist.js can be integrated easily with AngularJS,

React, Meteor, Ember and WordPress through a wide

range of wrapper libraries.

n3-charts

, is a Java script library for data visual-

ization built on D3.js. It offers simple and interactive

charts,

Sigma JS

is a library that offers interactiv-

http://addepar.github.io/ember-charts/

https://developers.google.com/chart/

http://www.fusioncharts.com/

http://www.chartjs.org/

http://leaﬂetjs.com/

https://gionkunz.github.io/chartist-js/

https://github.com/n3-charts

http://sigmajs.org/

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ity with mouse and touch support, refreshing and

rescaling, and renders on WebGL by default with an

HTML5 Canvas fallback. The two data formats of

choice are JSON and GEXF. Their plugin assortment

for interactivity is massive. Sigma JS is a rendering

engine specialized on drawing networks and graphs

on web pages with a high customizability. Is scales

well by supporting Big Data network.

Polymaps

, is a Javascript library for visualizing

maps. Polymaps uses SVG to represent geographi-

cal data from country-wide level all the way down

to local street. the user can use CSS rules to style

her visualization and data can be easily interpreted by

Polymaps via the GeoJSON standard. This tool is par-

ticularly interesting when creating heatmaps. All the

maps can be interactive and it is possible to visualize

cartography from OpenStreetMap, CloudMade, Bing

and many other maps providers.

Processing.js

, is a JavaScript library that sits on

top of the Processing visual programming language.

As every JavaSript library is, Processing.js is web ori-

ented and lets the user bring the Processing power

to her web pages. It requires an HTML5-compatible

browser.

dygraphs

is a fast, ﬂexible open source

JavaScript charting library. It allows users to explore

and interpret dense data sets. All the charts are inter-

active: it can be used mouse over to highlight individ-

ual values, or click and drag to zoom. It is possible

to change the number and hit enter to adjust the aver-

aging period. Dygraphs handles huge data sets: plots

millions of points without getting bogged down.

3.1.2 Data Visualization Tools

Within the North East region of ﬁgure 2, have been

collected charts-oriented web tools or desktop ap-

plications together with platforms for visualizing

large networks or graphs and data visualization tools

equipped with connectors for interfacing them to the

mainstream computing environments such as Matlab

or R. To this category also belong Plottly already de-

scribed and the following surveyed tools:

Timeline

, is an open-source tool that enables

anyone to build visually rich, interactive timelines. It

is possible to create a timeline using nothing more

than a Google spreadsheet. Experts can use their

JSON skills to create custom installations, while

keeping TimelineJS’s core functionality.

http://polymaps.org/

http://processingjs.org/

http://dygraphs.com/

http://timeline.knightlab.com/

Canvas

, is a Javascript charting library with a

simple API design and comes with a bunch of themes.

It is a lot faster than the conventional SVG or Flash

charts. It also comes with a responsive design so that

it can run on various devices like Android, iPhone,

Tablets, Windows, Mac etc. The chart gallery con-

sists of 24 different types of charts. Canvas can render

100000 data points in just 100 milliseconds. There-

fore, it represents a high-performancejavascript chart,

between the other libraries existing in the literature.

This tool is free for non-commercial usage.

Commetrix

, is a Software Framework for Dy-

namic Network Visualization and Analysis that sup-

ports Community Moderators, Members, and Net-

work Researchers. Commetrix is an exploratory anal-

ysis tool for dynamic network data. Its connectors can

conveniently read all sources of accessible network

data, like co-authorship or business process networks.

Still, the focus is on analyzing evolving patterns of

electronic communication, including e-mail, discus-

sions, voice over IP, and instant messaging.

Speciﬁcally concerning the graphs or networks

representation the list of surveyed tools is the follow-

ing:

Cuttleﬁsh

, is a network workbench application

that visualizes the networks with some of the best

known layout algorithms. It allows detailed visual-

izations of the network data, interactive manipulation

of the layout, graph edition and process visualization

as well as different input methods and outputs in TeX

using Tikz and PSTricks. It can be downloaded as jar

archive and can used with Gephi too.

Cytoscape

(Shannon et al., 2003), is an open

source software platform for visualizing molecular in-

teraction networks and biological pathways and inte-

grating these networks with annotations, gene expres-

sion proﬁles and other state data. Although Cytoscape

was originally designed for biological research, now

it is a general platform for complex network analy-

sis and visualization. Its core distribution provides

a basic set of features for data integration, analysis,

and visualization. Additional features are available

as Apps (formerly called Plugins) making Cytoscape

easily extendible. Apps are available for network and

molecular proﬁling analyses, new layouts, additional

ﬁle format support, scripting, and connection with

databases. Most of the Apps are freely available from

Cytoscape App Store.

Gephi

(Bastian et al., 2009) is an interactive vi-

http://canvasjs.com/

http://www.commetrix.de/

http://cuttleﬁsh.sourceforge.net/

http://www.cytoscape.org

https://gephi.org/

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301

sualization and exploration platform for all kinds of

networks and complex systems, dynamic and hierar-

chical graphs. Runs on Windows, Linux and Mac OS

X and is open-source and free. Gephi is a tool for

people that have to explore and understand graphs.

Like Photoshop but for data, the user interacts with

the representation, manipulate the structures, shapes

and colors to reveal hidden properties.

Graph-tool

(Peixoto, 2014), is an efﬁcient

Python module for manipulation and statistical anal-

ysis of graphs (a.k.a. networks). Contrary to most

other python modules with similar functionality, the

core data structures and algorithms are implemented

in C++, making extensive use of template meta-

programming, based heavily on the Boost Graph Li-

brary. Graph-tool has its own layout algorithms and

versatile, interactive drawing routines based on cairo

and GTK+, but it can also work as a very comfortable

interface to the excellent graphviz package.

Graphviz

(Gansner and North, 2000), is an

open source graph visualization software used for rep-

resenting structural information as diagrams of ab-

stract graphs and networks. The Graphviz layout pro-

grams take descriptions of graphs in a simple text lan-

guage, and make diagrams in useful formats, such as

images and SVG for web pages, PDF or Postscript

for inclusion in other documents; or display in an in-

teractive graph browser. Graphviz has many useful

features for concrete diagrams, such as options for

colors, fonts, tabular node layouts, line styles, hyper-

links, and custom shapes.

JUNG

(Java Universal Network/Graph Frame-

work) (O’Madadhain et al., 2005) is a software li-

brary that provides a common and extensible lan-

guage for the modelling, analysis, and visualization

of data that can be represented as a graph or network.

The JUNG architecture is designed to support a va-

riety of representations of entities and their relations,

such as directed and undirected graphs, multi-modal

graphs, graphs with parallel edges, and hypergraphs.

It provides a mechanism for annotating graphs, enti-

ties, and relations with metadata.

Keynetiq

, is a platform for Organizational Net-

work Analysis. It is cut off to design maps, visualize

and analyze networks of people and relations between

them, revealing how organizations really operate in

day-to-day business. It provide a complete range of

services ranging from the analysis of your objectives

and the design of an ONA study, implementation of

the Keynetiq tool, coordination of data gathering and

https://graph-tool.skewed.de/

http://graphviz.org/

http://jung.sourceforge.net/

https://www.keynetiq.com/

administration of the platform, right up to a full anal-

ysis of your organizational network and a detailed re-

port complete with an action plan.

Netlytic

, is a cloud-based text and social net-

works analyzer that can automatically summarize

large volumes of text and discover social networks

from online conversations on social media sites such

as Twitter, Youtube, blogs, online forums and chats.

Multi-tier subscription including free version.

NetMiner

(Ghim et al., 2014) is an application

software for exploratory analysis and visualization of

large network data based on SNA. It has embed inter-

nal Python-based script engine which equipped with

the automatic Script Generator. NetMiner 4 license

for coursework is provided to students and teachers.

Network Workbench

, is a Large-Scale Net-

work Analysis, Modeling and Visualization Toolkit

for Biomedical, Social Science and Physics Research.

This project will design, evaluate, and operate a dis-

tributed, shared resources environment for large-scale

network analysis, modeling, and visualization, named

Network Workbench (NWB).

NodeXL

(Smith et al., 2009), is a free, open-

source template for Microsoft Excel that makes it easy

to explore network graphs. With NodeXL, users can

enter a network edge list in a worksheet, click a but-

ton and see a graph, all in the environment of the Ex-

cel window. It provides ﬂexible import and export

graphs in GraphML, Pajek, UCINet, and matrix for-

mats. Furthermore, it allows direct connections to So-

cial Networks such as Twitter, YouTube, Flickr,etc.,

and, by using one of several available plug-ins, it is

able to import networks from Facebook, Exchange,

Wikis and WWW hyperlinks.

Pajek

(Smith et al., 2009) is a software, for

Windows, based on the motivation that there exist

several sources of large networks that are already in

machine-readable form. Pajek provides tools for anal-

ysis and visualization of such networks: collaboration

networks, organic molecule in chemistry, protein re-

ceptor interaction networks, genealogies, Internet net-

works, citation networks, diffusion (AIDS, news, in-

novations)networks, data-mining (2-mode networks),

etc. It is extensively used in academic research.

Statnet

(Handcocket al., 2008) is a suite of soft-

ware packages in R for network analysis of the statis-

tical modeling of networks. The analytic framework

is based on Exponential family Random Graph Mod-

https://netlytic.org/home/

http://www.netminer.com/main/main-read.do

http://nwb.cns.iu.edu/

http://nodexl.codeplex.com/

http://mrvar.fdv.uni-lj.si/pajek/

http://statnetproject.org/

KomIS 2017 - Special Session on Knowledge Discovery meets Information Systems: Applications of Big Data Analytics and BI -

methodologies, techniques and tools

302

els (ergm). It provides a comprehensive framework

for ergm-based network modelling, including tools

for model estimation, model evaluation, model-based

network simulation, and network visualization. This

broad functionality is powered by a central Markov

chain Monte Carlo (MCMC) algorithm.

Tulip

(Auber, 2004) is an information visualisa-

tion framework dedicated to the analysis and visuali-

sation of relational data. It aims to provide the devel-

oper with a complete library, supporting the design

of interactive information visualisation. Written in

C++ the framework enables the development of algo-

rithms, visual encodings, interaction techniques, data

models, and domain-speciﬁc visualisations. One of

the goal of Tulip is to facilitate the reuse of compo-

nents and allows the developers to focus on program-

ming their application. This development pipeline

makes the framework efﬁcient for research prototyp-

ing as well as the development of end-user applica-

tions.

Visone

(Baur et al., 2001) is a software for the

visual creation, transformation, exploration, analysis,

and representation of network data, jointly developed

at the University of Konstanz and the Karlsruhe Insti-

tute of Technology The main purpose of the Visone

software is to empower researchers in the social sci-

ences to analyze and visualize network data in an inte-

grated fashion. Potential applications range from so-

ciometry to bibliometrics and web analysis.

3.1.3 Scientiﬁc Visualization Tools

Within the North West region of ﬁgure 2, have been

collected the tools for Scientiﬁc Visualization such

as processing system for high scalability with dis-

tributed systems (e.g., Giraph) and other speciﬁc sci-

entiﬁc tools like the SDSS Navigation map from the

SDSS project. This category has been added in this

work for the sake of completeness but it is beyond the

scope of this survey, because Scientiﬁc Visualization

tools (like SDSS Navigation map) are created ad hoc

within scientiﬁc research projects to address speciﬁc

needs inside the researchers community, while the fo-

cus of this survey is on the general purpose tools land-

scape. For this reason, this section does not provide

any tools other than the already mentioned ones.

3.1.4 Business Intelligent and Visualization

Tools

Within the South West area of ﬁgure 2, have been col-

lected the tools for business Intelligent and visualiza-

http://tulip.labri.fr/TulipDrupal/

http://visone.info/html/demo.html

tion, for data analysis and predictive modelling with

visualization packages such as Weka and the follow-

ing

SocNetV

(Social Networks Visualizer) (Kala-

maras, 2014) is a cross-platform, user-friendly tool

for the analysis and visualization of Social Networks.

It lets the user to construct networks (mathematical

graphs) on a virtual canvas, or load networks of vari-

ous formats (GraphML, GraphViz, Adjacency, Pajek,

UCINET, etc). Also, SocNetV enables you to modify

the social networks, analyse their social and mathe-

matical properties and apply visualization layouts.

Sentinel Visualizer

is used for Advanced Link

Analysis, Data Visualization, Geospatial Mapping,

and SNA. Its database driven data visualization plat-

form lets the user quickly see multi-level links among

entities and model different relationship types. Ad-

vanced drawing and redrawing features generate opti-

mized views to highlight the most important entities.

4 CONCLUSIONS

This work has provided a ﬁrst evaluation of the most

spread solutions existing in the Big Data visualization

landscape. As shown in the previous sections, a great

number of solutions are open-source projects demon-

strating the great interest that the community of devel-

opers has in such topics. At the same time, the work

has highlighted the ﬂexibility of the most part of tools

that are generally multi-platform or programming lan-

guage agnostic as they are provided with HTTP Rest-

full APIs which allow clients to easily access them.

In other cases, the great availability of APIs writ-

ten in the most popular programming languages (in

most cases developed by third parties as depending

or separate projects) contribute yet to ease the inter-

operability between the client tools and the back-end

store database or mainstream analytics and comput-

ing tools. Future works can be directed to different

objectives. On the one hand, it can be improved the

evaluation framework by adding other criteria not yet

considered in this work, like the security and trusti-

ness of data, and a quantitative analysis over the scal-

ability performances. These criteria will be useful to

choose one solution over another. On the other hand,

new but complementary study can be approached by

surveying the technological solutions existing to deal

with other challenges of Big Data, such as: analytics,

heterogeneity, timeliness, integration and transfer.

http://socnetv.org/

http://www.fmsasg.com/

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303

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Table 1: Evaluation synopsis of selected technical and non-technical features for the surveyed visualization tools.

Name Usage Software cat-

egory

Visualization

structure

O.S. Licence Scalability Extensibility Latest version

(date)

Tableau Presentation Desktop App.,

cloud hosted

Charts, graphs,

maps, etc

Windows 7

or later, OSX

10.10 or later

Commercial

and Academic

license

Hadoop,

etc.

DBs Drivers 10.2 (Apr 12,

2017)

Infogram Presentation Desktop App.,

cloud hosted

charts, map,

images and

even videos

Windows 7

or later, OSX

10.10 or later

Commercial

and educa-

tional license

- - -

ChartBlocks Presentation JavaScript

library

charts (bar,

line, pie, etc.)

All web-based Commercial

(pay as you

go)

- - -

Plottly Presentation

and devel-

opers

Web tool,

JavaScript and

Python library

charts, plot,

maps

All web-based Commercial

and Commu-

nity

Cloud API for Mat-

lab, R, Python

and Javascript

D3.js Developers JavaScript

library

charts, plot,

maps

All web-based Open-source Cloud Javascript 4.8.0 (-)

Ember-charts Developers JavaScript

library

charts All web-based Open-source Cloud - 1.2.1 (Mar,

2017)

Google charts Developers JavaScript

library

charts,tree

map, timeline,

gauge

All web-based Open-source Cloud e Chart Tools

Datasource

protocol

45 (September

12, 2016)

Fusion

Charts

Developers JavaScript

library

charts All web-based Commercial - jQuery,

ASP.NET,

PHP

Chart.js Developers JavaScript

library

chart All web-based Open source - -

Leaﬂet Developers JavaScript

library

map All web-based open source - Extensive plu-

gin repository

Chartist.js Developers JavaScript

library

chart pie gauge All web-based open source - -

n3-charts Developers JavaScript

library

charts All web-based open source - -

Sigma JS Developers JavaScript

library

graphs, net-

works

All web-based open source - public API 1.2.0 (Nov 3,

2016)

Polymaps Developers JavaScript

library

maps All web-based open source - - 2.5.1 (April

10, 2011)

Processing.js Developers JavaScript

library

images All web-based open source - - 1.4.8 (March

25, 2014)

Dygraphs Developers JavaScript

library

charts All web-based open source - - 2.0.0 (June,

2015)

Timeline JS Developers Web applica-

tion

timeline All web-based open source - - -

Canvas Developers Web applica-

tion

chart Web-based,

Android, iOS

Commercial - Spring MVC,

Asp.net, PHP

1.9.8

Commetrix Presentation Software

framework

graph, net-

work

Windows Commercial - - 2.3

Cuttleﬁsh Presentation Software

framework

graph, net-

work

JVM-based

multiplatform

Open source - - -

Cytoscape Presentation Software

framework

graph, net-

work

Mac OS X

Windows

64bit, Linux

Open source Cytoscape

as a Ser-

vice

Cytoscape

open APIs for

plugins

3.5.1 (March,

2017)

Gephi Presentation Software

framework

graph, net-

work

Windows,

Mac OS X and

Linux

Open source - Gephi Plugins

and APIs

0.9.1 (Feb,

2016)

graph-tool Developer Python mod-

ule

graph, net-

work

Windows,

Mac OS X and

Linux

Open source - APIs 2.22

Graphviz Presenter Desktop

Application

graph, net-

work

JVM-based

multiplatform

Open source - - 2.38

JUNG Developer Java library graph, net-

work

JVM-based

multiplatform

Open source - - 2.0.1 (Jan,

2010)

Keynetiq Presentation Software

framework

graph, net-

work

- Commercial - - -

Netlytic Presentation Software

framework

graph, net-

work

- Commercial - - -

NetMiner Presentation Desktop

Application

graph, net-

work (seman-

tic networks)

Windows 32-

64 bit

Commercial Huge

License

- -

Network

Workbench

Presentation Desktop

Application

graph, net-

work

Windows 32-

64 bit

Commercial - - 1.0.0 (Sep 15,

2009)

NodeXL Presentation Template for

Microsoft

Excel

graph, net-

work

Windows 32-

64 bit

open-source - - 2014 (Jan,

2014)

Pajek Presentation Desktop

Application

graph, net-

work

Windows 32-

64 bit

open-source - - 5.01 (Feb,

2017)

SocNetV Presentation Desktop

Application

graph, net-

work

Cross-

Platform

Free and Open

source

- - 2.2 (Jan, 2017)

Sentinel Visu-

alizer

Presentation Desktop

Application

network,

graph, charts,

3D disp.

Cross-

Platform

Commercial - Add-ons 7.3 (Feb,

2017)

Statnet Presentation Desktop

Application

network,

graph

Cross-

Platform

Free, open

source

- - (2017)

Tulip Presentation Software

framework

relational data Windows Free, open

source

- Tulip Python

module

4.10.0

Visone Presentation Desktop

Application

network,

graph

Windows,

Linux, and

MacOS

Commercial

free for aca-

demic

- - 2.17 (Feb,

2017)

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