Visualizing Big Data with augmented and virtual reality: challenges and research agenda
(La visualización de Big Data con la realidad aumentaday virtual: desafíos yagenda de investigación)
Fuente: SPRINGER OPEN
Autores:Ekaterina Olshannikova,Aleksandr Ometov, Yevgeni Koucheryavy and Thomas Olsson
Este documento proporciona una visión general multidisciplinar de los temas de investigación y logros en el campo del Big Data y sus técnicas y herramientas de visualización. El objetivo principal es una síntesis de desafíos en los métodos de visualización del Big Data existentes, así como para ofrecer nuevas soluciones a los problemas relacionados con el estado actual de la visualización del Big Data . En este trabajo se ofrece una clasificación de los tipos de datos existentes, métodos analíticos, técnicas y herramientas de visualización, con especial énfasis colocado en la topografía de la evolución de la metodología de la visualización en los últimos años. Basándose en los resultados, revelamos desventajas de los métodos de visualización existentes. A pesar del desarrollo tecnológico del mundo moderno, la intervención humana (interacción), el juicio y el pensamiento lógico son necesarios cuando se trabaja con Big Data. Por lo tanto, se evalúa el papel de las limitaciones de percepción humanos que involucran grandes cantidades de información. Con base en los resultados, se propone un enfoque no tradicional: Se discute cómo las capacidades de Realidad Aumentada y Realidad Virtual se podrían aplicar al campo del Big Data. Se discute la utilidad de la integración prometedora tecnología de Realidad Mixta con aplicaciones en la visualización de l Big Data. La colocación de los datos más esenciales en la zona central del campo visual humano en realidad mixta permitiría que sea posible obtener la información que se presenta en un corto período de tiempo sin pérdida de datos importantes debido a problemas de percepción humana. Además, se discuten los impactos de las nuevas tecnologías, tales como las pantallas de realidad virtual y los cascos de Realidad Aumentada en la visualización de Big Data , así como a la clasificación de los principales retos de la integración de la tecnología.
This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.
Big DataVisualizationVirtual realityAugmented realityMixed realityHuman interactionEvolution
whole history of humanity is an enormous accumulation of data.
Information has been stored for thousands of years. Data has become an
integral part of history, politics, science, economics and business
structures, and now even social lives. This trend is clearly visible in
social networks such as Facebook, Twitter and Instagram where users
produce an enormous stream of different types of information daily
(music, pictures, text, etc.) .
Now, government, scientific and technical laboratory data as well as
space research information are available not only for review, but also
for public use. For instance, there is the 1000 Genomes Project [2, 3], which provide 260 terabytes of human genome data. More than 20 terabytes of data are publicly available at Internet Archive [4, 5], ClueWeb09 , among others.
Big Data processing has become more affordable for companies from
resource and cost points of view. Simply put, revenues generated from it
are higher than the costs, so Big Data processing is becoming more and
more widely used in industry and business . According to International Data Corporation (IDC), data trading is forming a separate market .
Indeed, 70 % of large organizations already purchase external data, and
it is expected to reach 100 % by the beginning of 2019.
Simultaneously, Big Data characteristics such as volume, velocity, variety , value and veracity 
require quick decisions in implementation, as the information may
become less up to date and can lose value fast. According to IDC ,
data volumes have grown exponentially, and by 2020 the number of
digital bits will be comparable to the number of stars in the universe.
As the size of bits geminates every two years, for the period from 2013
to 2020 worldwide data will increase from 4.4 to 44 zettabytes. Such
fast data expansion may result in challenges related to human ability to
manage the data, extract information and gain knowledge from it.
complexity of Big Data analysis presents an undeniable challenge:
visualization techniques and methods need to be improved. Many companies
and open-source projects see the future of Big Data Analytics via
Visualization, and are establishing new interactive platforms and
supporting research in this area. Husain et al. 
in their paper provide a wide list of contemporary and recently
developed visualization platforms. There are commercial Big Data
platforms such as International Business Machines (IBM) Software , Microsoft , Amazon  and Google . There exists an open-source project, Socrata ,
which deals with dynamic data from public, government and private
and social media generate enormous amounts of data every day and,
traditionally, represent it in a format consistent with the poorly
structured databases: web blogs, text documents, or machine code, such
as geospatial data that may be collected in various stores even outside
of a company/organization .
On the other hand, information stored in a multitude repository and the
use of cloud storage or data centers is also widely common .
Furthermore, companies have the necessary tools to establish the
relationship between data segments in addition to the process of making
the basis for meaningful conclusions. As data processing rates are
growing continuously, a situation may appear when traditional analytical
methods would not be able to stay up to date, especially with the
growing amount of constantly updated data, which ultimately opens the
way for Big Data technologies .
paper provides information about various types of existing data to
which certain techniques are useful for the analysis. Recently, many
visualization methods have been developed for a quick representation of
data that is already preprocessed. There has been a step away from
planar images towards multi-dimensional volumetric visualizations.
However, Big Data visualization evolution cannot be considered as
finished, inasmuch as new techniques generate new research challenges
and solutions that will be discussed in the following paper.
activity in the field of Big Data visualization is focused on the
invention of tools that allow a person to produce quick and effective
results working with large amounts of data. Moreover, it would be
possible to assess the analysis of the visualized information from all
the angles in novel, scalable ways. Based on Big Data related
literature, we identify the main visualization challenges and propose a
novel technical approach to visualize Big Data based on the
understandings of human perception and new Mixed Reality (MR)
technologies. From our perspective, one of the more promising methods
for improving upon current Big Data visualization techniques is in its
correlation with Augmented Reality (AR) and Virtual Reality (VR) that
are suitable for the limited perception capabilities of humans. We
identify important steps for the research agenda to implement this
This paper covers various issues and topics, but there are three main directions of this survey:
Human cognitive limitations in terms of Big Data Visualization.
Applying Augmented and Virtual reality opportunities towards Big Data Visualization.
Challenges and benefits of the proposed visualization approach.
rest of paper is organized as follows: The first section provides a
definition of Big Data and looks at currently used methods for Big Data
processing and their specifications. Also it indicates the main
challenges and issues in Big Data analysis. Next, in the section
Visualization methods, the historical background of this field is given,
modern visualization techniques for massive amounts of information are
presented and the evolution of visualization methods is discussed.
Further in the last section, Integration with Augmented and Virtual
Reality, the history of AR and VR is detailed with respect to its
influence on Big Data. These developmental processes are supported by
the proposed oncoming Big Data visualization extension for VR and AR,
which can solve actual perception and cognition challenges. Finally,
important data visualization challenges and future research agenda are
Big Data: an overview
large data sources are ubiquitous throughout the world. Data used for
processing may be obtained from measuring devices, radio frequency
identifiers, social network message flows, meteorological data, remote
sensing, location data streams of mobile subscribers and devices, and
audio and video recordings. So, as Big Data is more and more used all
over the world, a new and important research field is being established.
The mass distribution of the technology and innovative models that
utilize these different kinds of devices and services, appeared to be a
starting point for the penetration of Big Data in almost all areas of
human activity, including the commercial sector and public
Big Data and the continuing dramatic increase in human and
machine-generated data associated with it are quite evident. However, do
we actually know what Big Data is, and how close are the various definitions put forward for this
term? For instance, there was a article in Forbes in 2014 which is
related to this controversial question .
It gave a brief history of the establishment of the term, and provided
several existing explanations and descriptions of Big Data to improve
the core understanding of the phenomenon. On the other hand, Berkeley
School of Information published a list with more than 40 definitions of
the term .
Big Data covers various fields and sectors, the meaning of this term
should be specifically defined in accordance with the activity of the
specific organization/person. For instance, in contrast to
industry-driven Big Data “V’s” definitions, Dr. Ivo Dinov for his
research scope listed another data’s multi-dimensional characteristics  such as data size, incompleteness, incongruency, complex representation, multiscale nature and heterogeneity of its sources [31, 32].
In this paper the modified Gartner Inc. definition [33, 34] is used: Big
Data is a technology to process high-volume, high-velocity,
high-variety data or data-sets to extract intended data value and ensure
high veracity of original data and obtained information that demand
cost-effective, innovative forms of data and information processing
(analytics) for enhanced insight, decision making, and processes control .
Big Data processing methods
Currently, there exist many different techniques for data analysis ,
mainly based on tools used in statistics and computer science. The most
advanced techniques to analyze large amounts of data include:
artificial neural networks [37–39]; models based on the principle of the organization and functioning of biological neural networks [40, 41]; methods of predictive analysis ; statistics [43, 44]; Natural Language Processing ;
etc. Big Data processing methods embrace different disciplines
including applied mathematics, statistics, computer science and
economics. Those are the basis for data analysis techniques such as Data
Mining [39, 46–49], Neural Networks [41, 50–52], Machine Learning [53–55], Signal Processing [56–58] and Visualization Methods [59–61].
Most of these methods are interconnected and used simultaneously during
data processing, which increases system utilization tremendously (see
would like to familiarize reader with the primary methods and
techniques in Big Data processing. As this topic is not a focus of the
paper, this list is not exhaustive. Nevertheless, the main
interconnections between these methods are shown and application
examples are given.
Optimization methods are mathematical tools for efficient data analysis.
Optimization includes numerical analysis focused on problem solving in
various Big Data challenges: volume, velocity, variety and veracity  that will be discussed in more detail later. Some widely used analytical techniques are genetic programming [63–65], evolutionary programming  and particle swarm optimization [67, 68].
Optimization is focused on the search of the optimal set of actions
needed to improve system performance. Notably, genetic algorithms are
also a specific part of machine learning direction . Moreover, statistical testing, predictive and simulation models are applied also as for Statistics methods .
Statistics methods are used to collect, organize and interpret data, as well as to outline
interconnections between realized objectives. Data-driven statistical
analysis concentrates on implementation of statistics algorithms [71, 72]. A/B testing 
technique is an example of a statistics method. In terms of Big Data
there is a possibility to perform a variety of tests. The aim of A/B
tests is to detect statistically important differences and regularities
between groups of variables to reveal improvements. Besides, statistical
techniques contain cluster analysis, data mining and predictive
modelling methods. Some techniques in spatial analysis 
originate from the field of statistics as well. It allows analysis of
topological, geometric or geographic characteristics of data sets.
Data mining includes cluster analysis, classification, regression and association rule learning techniques. This method is aimed at identifying and extracting beneficial information from extensive data or datasets. Cluster analysis [75, 76] is based on principles of similarities to classify objects. This technique belongs to unsupervised learning [77, 78] where training data  is used. Classification 
is a set of techniques which are aimed at recognizing categories with
new data points. In contrast to cluster analysis, a classification
technique uses training data sets to discover predictive relationships. Regression 
is a set of a statistical techniques that are aimed at determining
changes between dependent and independent variables. This technique is
mostly used for prediction or forecasting. Association rule learning [82, 83] is set of techniques designed to detect valuable relationships or association rules among variables in databases.
Machine Learning is a significant area in computer science which aims to create
algorithms and protocols. The main goal of this method is to improve
computers’ behaviors on the basis of empirical data. Its implementation
allows recognition of complicated patterns and automatic application of
intelligent decision-making based on. Pattern recognition, natural
language processing, ensemble learning and sentiment analysis are
examples of machine learning techniques. Pattern recognition [84, 85]
is a set of techniques that use a certain algorithm to associate an
output value with a given input value. Classification technique is an
example of this. Natural language processing 
takes its origins from computer science within the fields of artificial
intelligence and linguistics. This set of techniques performs analysis
of human language. Sometimes it uses a sentiment analysis 
that is able to identify and extract specific information from text
materials evaluating words, degree and strength of a sentiment. Ensemble learning [88, 89]
in automated decision-making systems is a useful technique for
diminishing variance and increase accuracy. It aims to solve diverse
machine learning issues such as confidence estimation, missing feature
and error correction, etc.
Signal processing consists of various techniques that are part of electrical engineering
and applied mathematics. The key aspect of this method is the analysis
of discrete and continuous signals. In other words, it enables the
analog representation of physical quantities (e.g. radio signals or
sounds, etc.). Signal detection theory  is applied to evaluate the capacity for distinguishing between signal and noise in some techniques. A time series analysis [91, 92]
includes techniques from both statistics and signal processing.
Primarily, it is designed to analyze sequences of data points with a
demonstration of data values at consistent times. This technique is
useful to predict future data values based on knowledge of past ones.
Signal processing techniques can be applied to implement some types of data fusion .
Data fusion combines multiple sources to obtain improved information
that is more relevant or less expensive and has higher quality .
concern the design of graphical representation, i.e. to visualize the
innumerate amount of the analytical results as diagrams, tables and
images. Visualization for Big Data differs from all of the previously
mentioned processing methods and also from traditional visualization
techniques. To visualize large-scale data, feature extraction and
geometric modelling can be implemented. These processes are needed to
decrease the data size before actual rendering .
Intuitively, visual representation is more likely to be accepted by a
human in comparison with unstructured textual information. The era of
Big Data has been rapidly promoting the data visualization market.
According to Mordor Intelligence 
the visualization market will increase at a compound annual growth rate
(CAGR) of 9.21 % from $4.12 billions in 2014 to $6.40 billions by the
end of 2019. SAS Institute provides results of an International Data
Group (IDG) research study in the white paper .
The research is focused on how companies are performing Big Data
analysis. It shows that 98 % of the most effective companies working
with Big Data are presenting results of the analysis via visualization.
Statistical data from this research provides evidence of the
visualization benefits in terms of decision-making improvement, better
ad-hoc data analysis, improved collaboration and information sharing
inside/outside an organization.
different groups of people including designers, software developers and
scientists are in the process of searching for new visualization tools
and opportunities. For example, Amazon, Twitter, Apple, Facebook and
Google are companies that utilize data visualization in order to make
appropriate business decisions .
Visualization solutions can provide insights from different business
perspectives. First of all, implementation of advanced visualization
tools enables rapid exploration of all customers/users data to improve
customer-company relationships. It allows marketers to create more
precise customer segments based on data from purchasing history or life
stage and other factors. Besides, correlation mapping may assist in the
analysis of customer/user behavior to identify and analyze the most
profitable of them. Secondly, visualization capabilities allow companies
opportunities to reveal correlations between product, sales and
customer profiles. Based on gathered metrics, organizations may provide
novel special offers to their customers. Moreover, visualization enables
tracking of revenue trends and can be useful for risk analysis.
Thirdly, visualization as a tool provides better understanding of data.
Higher efficiency is reached by obtaining relevant, consistent and
accurate information. So, visualized data could assist organizations to
find different effective marketing solutions. In this section we
familiarized the reader with the main techniques of data analysis and
described their strong correlation to each other. Nevertheless, the Big
Data era is still in the beginning stage of its evolution. Therefore,
Big Data processing methods are evolving to solve the problems of Big
Data and new solutions are continuously being developed. By this
statement we mean that big world of Big Data requires multiple
multidisciplinary methods and techniques that lead to better
understanding of the complicated structures and interconnections between
Big Data challenges
Big Data has some inherent challenges and problems that can be primarily divided into three groups according to Akerkar et al. : (1) data, (2) processing and (3) management challenges (see Fig. 2). While dealing with large amounts of information we face such challenges as volume, variety, velocity and veracity that are also known as 5V of Big Data. As those Big Data characteristics are well examined in scientific literature [99–101] we will only discuss them briefly. Volume
refers to the large amount of data, especially, machine-generated. This
characteristic defines a size of the data set that makes its storage
and analysis problematic utilizing conventional database technology. Variety
is related to different types and forms of data sources: structured
(e.g. financial data) and unstructured (social media conversations,
photos, videos, voice recordings and others). Multiplicity of the
various data results in the issue of its handling. Velocity
refers to the speed of new data generation and distribution. This
characteristic requires the implementation of real-time processing for
the streaming data analysis (e.g. on social media, different types of
transactions or trading systems, etc.). Veracity
refers to the complexity of data which may lead to a lack of quality
and accuracy. This characteristic reveals several challenges:
uncertainty, imprecision, missing values, misstatement and data
availability. There is also a challenge regarding data discovery that is related to the search of high quality data in data sets.
The second branch of Big Data challenges is called processing challenges.
It includes data collection, resolving similarities found in different
sources, modification data to a type acceptable for the analysis, the
analysis itself and output representation, i.e. the results
visualization in a form most suitable for human perception.
The last type of challenge offered by this classification is related to data management.
Management challenges usually refer to secured data storage, its
processing and collection. Here the main focuses of study are: data
privacy, its security, governance and ethical issues. Most of them are
controlled based on policies and rules provided by information security
institutes on state or international levels.
past generations, the results of analyzed data were represented as
visualized plots and graphs. It is evident that collections of complex
figures are sometimes hard to perceive, even by well-trained minds.
Nowadays, the main factors causing difficulties in data visualization
continue to be the limitations of human perception and new issues
related to display sizes and resolutions. This question is studied in
detail further in the section “Integration with Augmented and Virtual
Reality”. Preparatory to the visualization, the main interaction problem
is in the extraction of the useful portion of information from massive
volumes. Extracted data is not always accurate and mostly overloaded
with excrescent information. Visualization technique is useful for
simplifying information and transforming it into a more accessible form
for human perception.
the near future, petascale data may cause analysis failures because of
traditional approaches in usage, i.e. when the data is stored on a
memory disk continuously waiting for further analysis. Hence, the
conservative approach of data compressing may become ineffective in
visualization methods. To solve this issue, developers should create a
flexible tool for the practice of data collection and analysis.
Increases in data size make the multilevel hierarchy approach incapable
in data scalability. Hierarchy becomes complex and intensive, making
navigation difficult for user perception. In this case, a combination of
analytics and Data Visualization may enable more accessible data
exploration and interaction, which would allow improving insights,
outcomes and decision-making.
methods, techniques and tools for data analysis are still not flexible
enough to discover valuable information in the most efficient way. The
question of data perception and presentation remains open. Scientists
face the task of uniting the abstract world of data and the physical
world through visual representation. Meanwhile, visualization-based
tools should fulfill three requirements [102, 103]:
expressiveness (demonstrate exactly the information contained in the
data), effectiveness (related to cognitive capabilities of human visual
system) and appropriateness (cost-value ratio for visualization benefit
assessment). Experience of previously used techniques can be repurposed
to achieve more beneficial and novel goals in Big Data perception and
the primary areas of visualization were Science Visualization and
Information Visualization. However, during recent decades, the field of
Visual Analytics was actively developing.
As a separate discipline, visualization emerged in 1980  as a reaction to the increasing amount of data generated by computer calculations. It was named Science Visualization [105–107],
as it displays data from scientific experiments related to physical
processes. This is primarily a realistic three-dimensional
visualization, which has been used in architecture, medicine, biology,
meteorology, etc. This visualization is also known as Spatial Data
visualization, which focuses on the visualization of volumes and
Information Visualization [108–111]
emerged as a branch of the Human-Computer Interaction field in the end
of 1980s. It utilizes graphics to assist people in comprehending and
interpreting data. As it helps to form mental models of the data, for
humans it is easier to reveal specific features and patterns of the
Visual Analytics [112–114]
combines visualization and data analysis. It has absorbed features of
Information Visualization as well as Science Visualization. The main
difference from other fields is the development and provision of
visualization technologies and tools.
visualization tools should consider cognitive and perceptual properties
of the human brain. Visualization aims to improve the clarity and
aesthetic appeal of the displayed information and allows a person to
understand large amount of data and interact with it. Significant
purposes of Big Data visual representation are: to identify hidden
patterns or anomalies in data; to increase flexibility while searching
of certain values; to compare various units in order to obtain relative
difference in quantities; to enable real-time human interaction
(touring, scaling, etc.).
Visualization methods have evolved much over the last decades (see Fig. 3),
the only limit for novel techniques being human imagination. To
anticipate the next steps of data visualization development, it is
necessary to take into account the successes of the past. It is
considered that quantitative data visualization appeared in the field of
statistics and analytics quite recently. However, the main precursors
were cartography and statistical graphics, created before the 19th
century for the expansion of statistical thinking, business planning and
other purposes .
The evolution in the knowledge of visualization techniques resulted in
mathematical and statistical advances as well as in drawing and
the 16th century, tools for accurate observation and measurement were
developed. Precisely, in those days the first steps were done in the
development of data visualization. The 17th century was swept by the
problem of space, time and distance measurements. Furthermore, the study
of the world’s population and economic data had started.
18th century was marked by the expansion of statistical theory, ideas
of data graphical representation and the advent of new graphic forms. At
the end of the century thematic maps displaying geological, medical and
economic data was used for the first time. For example, Charles de
Fourcroy used geometric figures and cartograms to compare areas or
demographic quantities .
Johann Lambert (1728–1777) was a revolutionary person, who used
different types of tables and line graphs to display variable data . The first methods were performed as simple plots followed by one-dimensional histograms .
Still, those examples are useful only for small amounts of data. By
introducing more information, this type of diagram would reach a point
At the turn of 20–21st centuries, steps were taken in the development of interactive statistical computing  and new paradigms for data analysis .
Technological progress was certainly a significant prerequisite for the
rapid development of visualization techniques, methods and tools. More
precisely, large-scale statistical and graphics software engineering was
invented, and computer processing speed and capacity vastly increased .
the next step presenting a system with the addition of a time dimension
appeared as a significant breakthrough. In the beginning of the present
century few dimensional visualization methods were in use as a part
2D/3D node-link diagram .
Already at this level of abstraction, any user may classify the goal
and specify further analytical steps for the research, but
unfortunately, data scaling became an essential issue.
currently used technologies for data visualization are already causing
enormous resource demands which include high memory requirements and
extremely high deployment cost. However, the currently existing
environment faces a new limitation based on the large amounts of data to
be visualized in contrast to past imagination issue. Modern effective
methods are focused on representation in specified rooms equipped with
widescreen monitors or projectors .
there are a fairly large number of data visualization tools offering
different possibilities. These tools can be classified based on three
factors: by the data type, by visualization technique type and by the
interoperability. The first refers to the different types of data to be visualized :
Univariate data One dimensional arrays, time series, etc.
Two-dimensional data Point two-dimensional graphs, geographical coordinates, etc.
Multidimensional data Financial indicators, results of experiments, etc.
Texts and hypertexts Newspaper articles, web documents, etc.
Hierarchical and links The structure subordination in the organization, e-mails, documents and hyperlinks, etc.
Algorithms and programs Information flows, debug operations, etc.
The second factor is based on visualization techniques and samples to represent different types of data.
Visualization techniques can be both elementary (line graphs, charts,
bar charts) and complex (based on the mathematical apparatus).
Furthermore, visualization can be performed as a combination of various
methods. However, visualized representation of data is abstract and
extremely limited by one’s perception capabilities and requests (see
Fig. 4 ).
Types of visualization techniques are listed below:
2D/3D standard figure . May be implemented as bars, line graphs, various charts, etc. (see Fig. 5 ). The main drawback of this type is the complexity of the acceptable visualization for complicated data structures;
Geometric transformations . This technique represents information as scatter diagram (see Fig. 6).
This type is geared towards a multi-dimensional data set’s
transformation in order to display it in Cartesian and non-Cartesian
geometric spaces. This class includes methods of mathematical
Display icons .
Ruled shapes (needle icons) and star icons. Basically, this type
displays the values of elements of multidimensional data in properties
of images (see Fig. 7).
Such images may include human faces, arrows, stars, etc. Images can be
grouped together for holistic analysis. The result of the visualization
is a texture pattern, which varies according to the specific
characteristics of the data;
Methods focused on the pixels .
Recursive templates and cyclic segments. The main idea is to display
the values in each dimension into the colored pixel and to merge some of
them according to specific measurements (see Fig. 8).
Since one pixel is used to display a single value, therefore
visualization of large amounts of data can be reachable with this
Hierarchical images . Tree maps and overlay measurements (see Fig. 9). These type methods are used with the hierarchical structured data.
third factor is related to the interoperability with visual imagery and
techniques for better data analysis. The application used for the
visualization should present visual forms that capture the essence of
data itself. However, it is not always enough for a complete analysis.
Data representation should be constructed in order to allow a user to
have different visual points of view. Thus, the appropriate
compatibility should be performed:
Dynamic projection .
Non-static change of projections in multidimensional data sets is used.
An example of the dynamic projection in two-dimensional plane of
multidimensional data in a scatter plots. It is necessary to note that
the number of possible projections increases exponentially with the
number of measurements and, thus, perception suffers more.
Interactive filtering .
In the investigation of large amounts of data there is a need to share
data sets and highlight significant subsets in order to filter images.
Significantly, that there should be an opportunity to have a visual
representation in real time. A subset can be chosen either directly from
a list or by determining a subset of the properties of interest;
Scaling images .
Scaling is a well-known method of interaction used in many
applications. Especially for Big Data processing, this method is very
useful due to the ability to represent data in a compressed form. It
provides the ability to simultaneously display any part of an image in a
more detailed form. Nevertheless, a lower level entity may be
represented by a pixel at a higher level, a certain visual image or an
accompanying text label;
Interactive distortion 
supports the research process data using distortion scale with partial
detail. The basic idea of this method is that a part of the fine
granularity displayed data is shown in addition to one with a low level
of details. The most popular methods are hyperbolic and spherical
Interactive combination [134,135]
brings together a combination of different visualization techniques to
overcome specific deficiencies by their conjugation. For example,
different points of the dynamic projection can be combined with the
techniques of coloring.
summarize, any visualization method can be classified by data type,
visualization technique and interoperability. Each method can support
different types of data, various images and varied methods for
visual representation of Big Data analysis is crucial for its
interpretation. As it was already mentioned, it is evident that human
perception is limited. The main purpose of modern data representation
methods is related to improvement in forms of images, diagrams or
animation. Examples of well known techniques for data visualization are
presented below :
Tag cloud 
is used in text analysis, with a weighting value dependent on the
frequency of use (citation) of a particular word or phrase (see Fig. 10).
It consists of an accumulation of lexical items (words, symbols or
combination of the two). This technique is commonly integrated with web
sources to quickly familiarize visitors with the content via key words.
is an imaging technique used in cluster analysis by means of
representing the relation of individual elements of the data as they
change their number (see Fig. 11). Choosing the optimal number of clusters is also an important component of cluster analysis.
Motion charts allow effective exploration of large and multivariate data and interact with it utilizing dynamic 2D bubble charts (see Fig. 12).
The blobs (bubbles—central objects of this technique) can be controlled
due to variable mapping for which it is designed. For instance, motion
charts graphical data tools are provided by Google , amCharts  and IBM Many Eyes .
Dashboard  enables the display of log files of various formats and filter data based on chosen data ranges (see Fig. 13). Traditionally, dashboard consists of three layers :
data (raw data), analysis (includes formulas and imported data from
data layer to tables) and presentation (graphical representation based
on the analysis layer)
there are many publicly available tools to create meaningful and
attractive visualizations. For instance, there is a chart of open
visualization tools for data visualization and analysis published by
Sharon Machils .
The author provides a list, which contains more than 30 tools from
easiest to most difficult: Zoho Reports, Weave, Infogr.am, Datawrapper
of these modern methods and tools follow fundamental cognitive
psychology principles and use the essential criteria of data successful
representation  such as manipulation of size, color and connections between visual objects (see Fig. 14). In terms of human cognition, the Gestalt Principles 
are relevant. The basis of Gestalt psychology is a study of visual
perception. It suggests that people tend to perceive the world in a form
of holistic ordered configuration rather than constituent fragments
(e.g. at first, person perceives forest and after that can identify
single trees as part of the whole). Moreover, our mind fills in the
gaps, seeks to avoid uncertainty and easily recognizes similarities and
differences. The main Gestalt principles such as law of proximity
(collection of objects forming a group), law of similarity (objects are
grouped perceptually if they are similar to each other), symmetry
(people tend to perceive object as symmetrical shapes), closure (our
mind tends to close up objects that are not complete) and figure-ground
law (prominent and recessed roles of visual objects) should be taken
into account in Big Data Visualization.
this end, the most effective visualization method is the one that uses
multiple criteria in the optimal manner. Otherwise, too many colors,
shapes, and interconnections may cause difficulties in the comprehension
of data, or some visual elements may be too complex to recognize.
observation and discussion about existing visualization methods and
tools for Big Data, we can clarify and outline its important disadvantages that are sufficiently discussed by specialists from different fields [147–149].
Various ways of data interpretation make them meaningful. It is easy to
distort valuable information in its visualization, because a picture
convinces people more effectively than textual content. Existing
visualization tools aim to create as simple and abstract images as
possible, which can lead to a problem when significant data can be
interpreted as disordered information and important connections between
data units will be hidden from the user. It is a problem of visibility
loss, which also refers to display resolution, where the quality of
represented data depends on number of pixels and their density. A
solution may be in the use of larger screens .
However, this concept brings a problem of human brain
cognitive-perceptual limitations, as will be discussed in detail in the
section Integration with Augmented and Virtual Reality.
visual and automated methods in Big Data processing gives a possibility
to use human knowledge and intuition. Moreover, it becomes possible to
discover novel solutions for complex data visualization .
Vast amounts of information motivate researchers and developers to
create new tools for quick and accurate analysis. As an example, the
rapid development of visualization techniques may be concerned. In the
world of interconnected research areas, developers need to combine
existing basic, effective visualization methods with new technological
opportunities to solve the central problems and challenges of Big Data
Integration with augmented and virtual reality
It is well known that the vision perception capabilities of the human brain are limited .
Furthermore, handling a visualization process on currently used screens
requires high costs in both time and health. This leads to the need of
its proper usage in the case of image interpretation. Nevertheless, the
market is in the process of being flooded with countless numbers of
wearable devices [153, 154] as well as various display devices [155, 156].
The term Augmented Reality was invented by Tom Caudell and David Mizel in 1992 and meant to
describe data produced by a computer that is superimposed to the real
Nevertheless, Ivan Sutherland created the first AR/VR system already in
1968. He developed the optical see-through head-mounted display that
can reveal simple three-dimensional models in real time . This invention was a predecessor to the modern VR displays and AR helmets  that seem to be an established research and industrial area for the coming decade . Applications for use have already been found in military , education , healthcare , industry  and gaming fields . At the moment, the Oculus Rift 
helmet gives many opportunities for AR practice. Concretely, it will
make it possible to embed virtual content into the physical world.
William Steptoe has already done research in this field. The use of it
in the visualization area might solve many issues from narrow visual
angle, navigation, scaling, etc. For example, offering a way to have a
complete 360-degrees view with a helmet can solve an angle problem. On
the other hand, a solution can be obtained with help of specific
widescreen rooms, which by definition involves enormous budgets.
Focusing on the combination of dynamic projection and interactive
filtering visualization methods, AR devices in combination with motion
recognition tools might solve a significant scaling problem especially
for multidimensional representations that comes to this area from the
field of Architecture. Speaking more precisely, designers (specialized
in 3D-visualization) work with flat projections in order to produce a
visual model .
However, the only option to present a final image is in moving around
it and thus navigation inside the model seems to be another influential
From the Big Data visualization point of view, scaling is a significant issue mainly caused by multidimensional systems where a
need to delve into a branch of information in order to obtain some
specific value or knowledge takes its place. Unfortunately, it cannot be
solved from a static point of view. Likewise, integration with motion
detection wearables  would highly increase such visualization system usability. For example, the additional use of an MYO armband  may be a key to the interaction with visualized data in the most native way. Similar comparison may be given as a pencil-case in which one tries to find a sharpener and spreads stationery with his/her fingers.
the use of AR displays and helmets is also limited by specific
characteristics of the human eye (visual system), such as field of view
and/or diseases like scotoma  and blind spots . Central vision 
is most significant and necessary for human activities such as reading
or driving. Additionally, it is responsible for accurate vision in the
pointed direction and takes most of the visual cortex in the brain but
its retinal size is less than 1 % .
Furthermore, it captures only two degrees of the vision field, which
stays the most considerable for text and object recognition.
Nevertheless, it is supported with Peripheral vision which is
responsible for events outside the center of gaze. Many researchers
around the world are currently working with virtual and AR to train
young professionals [175–177], develop new areas [178, 179] and analyze the patient’s behavior .
the well known topics like colorblindness, natural field of view and
other physiological abnormalities, recent research by Israel Abramov et
is overviewing physiological gender and age differences based on the
cerebral cortex and its large number of testosterone receptors ,
as a basis for the variety in perception procedures. The study was
mainly about the focused image onto the retina at the back of the
eyeball and its visual system processing. We overview the main reasons
for those differences, starting from prehistoric times, when African
habitats in forest regions had limited distance for object detection and
identification, thus obtained higher acuity for males may be explained.
Also, sex differences might be related to different roles in the
survival commune. So that males were mainly hunting (hunter-gatherer
hypothesis)—they had to detect enemies and predators much faster . Moreover, there are significant gender differences for far- and near-vision: males have their advantage in a far-space .
On the other hand, females are much more susceptible for brightness and
color changes in addition to static objects in near-space .
However, we can conclude that male/female differences in the sensory
capacities are adaptive but should be considered in order to optimize
represented and visualized data for end-uses. Additionally, there exists
a research area focusing on the human eye movement patterns during the
perception of scenes and objects. It can be based on different factors
starting from particular culture peculiar properties  and up to specific search tasks  being in high demand for Big Data visualization purposes.
studies shall be focused on the usage of ophthalmology and neurology
for the development of the new visualization tools. Basically, such
cross-discipline collaboration would support decision making for the
image position selection, which is mainly related to the problem of the
significant information losses due to the vision angle extension.
Moreover, it is highly important to take in account current hardware
quality and screens resolution in addition to the software part.
Nevertheless, there is a need of the improvement for multicore GPU
processors besides the address bus throughput refinement between CPU and
GPU or even replacement for wireless transfer computations on cluster
systems. Never the less, it is significant to discuss current
visualization challenges to support future research.
Future research agenda and data visualization challenges
data can significantly improve the understanding of the preselected
information for an average user. In fact, people start to explore the
world using visual abilities since birth. Images are often more easier
to perceive in comparison to text. In the modern world, we can see clear
evolution towards visual data representation and imagery experience.
Moreover, visualization software becomes ubiquitous and publicly
available for ordinary user. As a result, visual objects are widely
distributed—from social media to scientific papers and, thus, the role
of visualization while working with large amount of data should be
reconsidered. In this section, we overview important challenges and
possible solutions related to future agenda for Big Data visualization
with AR and VR usage:
Application development integration
In order to operate with visualized objects, it is necessary to create a
new interactive system for the user. It should support such actions as:
scaling; navigating in visualized 3D space; selecting sub-spaces,
objects, groups of visual elements (flow/path elements) and views;
manipulating and placing; planning routes of view; generating,
extracting and collecting data (based on the reviewed visualized data). A
novel system should allow multimodal control by voice and/or gestures
in order to make it more intuitive for users as it is shown in [188–190] and .
Nevertheless, one of the main issues regarding this direction of
development is the fact that implementing effective gestural and voice
interaction is not a trivial matter. There is a need to develop a machine learning system and to define basic intuitive gestures that are currently in research for general [192–194] and more specific (medical) purposes .
Equipment and virtual interface It is necessary to apply certain equipment for the implementation of
such an interactive system in practice. Currently, there are optical and
video see-trough head-mounted displays (HMD)  that merge virtual objects into the real scene view. Both have the following issues: distortion and resolution of the real scene; delay of a system; viewpoint matching; engineering and cost factors. As for the interaction issue, for an appropriate haptic feedback in an MR environment there is a need to create a framework that would allow an interaction with intuitive gesture. As it is revealed in the section Integration with Augmented and Virtual Reality, glove-based systems 
are mainly used for virtual object manipulation. The disadvantage of
hand-tracking input is so that there is no tactile feedback. In summary,
the interface should be redesigned or reinvented in order to simplify
user interaction. Software engineers should create new approaches,
principles and methods in User Interface Design to make all instruments
easily accessible and intuitive to use.
Tracking and recognition system Objects and tools have to be tracked in virtual space. The position and orientation values of virtual items are dynamic and have to be re-estimated during presentation. Tracking head movement
is another significant challenge. It aims to avoid mismatch of the real
view scene and computer generated objects. This challenge may be solved
by using more flexible software platforms.
Perception and cognition
Actually, the level of computer operation is high but still not
sufficiently effective in comparison to human brain performance even in
cases of neural networks. As was mentioned earlier in the section
Integration with Augmented and Virtual Reality, human perception and cognition
have their own characteristics and features, and the consideration of
this issue by developers during hardware and interface design for AR is
vital. In addition, the user’s ability to recognize and understand the data is a central issue. Tasks such as browsing and searching require a
certain cognitive activity. Also, there can be issues related to
different users’ reactions with regard to visualized objects depending
on their personal and cultural backgrounds. In this sense, simplicity in
information visualization has to be achieved in order to avoid
misperceptions and cognitive overload .
Psychophysical studies would provide answers to questions regarding
perception and would give the opportunity to improve performance by
Virtual and physical objects mismatch
In an Augmented Reality environment, virtual images integrate with real
world scenery at the static distance in the display while the distance
to real objects varies. Consequently, a mismatch of virtual and physical distances is irreversible and it may result in incorrect focus, contrast and
brightness of virtual objects in comparison to real ones. The human eye
is capable of recognizing many levels of brightness, saturation and
but most contemporary optical technologies cannot display all levels
appropriately. Moreover, potential optical illusions arise from
conflicts between computer-generated and real environment objects. Using
modern equipment would be a solution for this challenge.
With the current technology development level, visualized information
is presented mainly on screens. Even a VR helmet is equipped with two
displays. Unfortunately, and because of the close-to-the-eye proximity,
users can experience lack of comfort
while working with it. It is mainly based on a low display resolution
and high graininess and, thus, manufacturers should take it into
consideration for further improvement.
Education As this concept is relatively new, there is a need to specify the value
of the data visualization and its contribution to the users’ work. The
value cannot be so obvious; that is why compelling showcase examples and
publicly available tutorials can reveal AR and VR potential in visual
analytics. Moreover, users need to be educated and trained for the oncoming interaction with this evolving technology. The visual
literacy skill should be improved in order to have high performance
while working with visualized objects. A preferable guideline can be
chosen as Visual Information-Seeking Mantra: overview first, zoom and
filter, then details on demand .
all the challenges, the main benefit from the implementation of MR
approach is human experience improvement. At the same time, such
visualization allows convenient access to huge amounts of data and
provides a view from different angles. The navigation is smooth and
natural via tangible and verbal interaction. It also minimizes
perceptional inaccuracy in data analysis and makes visualization
powerful at conveying knowledge to the end user. Furthermore, it ensures
actionable insights that improves decision making.
conclusion, challenges of data visualization for AR and VR are
associated not only with current technology development but also with
human-centric issues. Interestingly, some researchers are already
working on the conjugation of such complex fields as massive data
analysis, its visualization and complex control of the visualized
It is worthwhile to note that those factors should be taken into
account simultaneously in order to achieve the best outcome for the
established industrial field.
practice, there are a lot of challenges for Big Data processing and
analysis. As all the data is currently visualized by computers, it leads
to difficulties in the extraction of data, followed by its perception
and cognition. Those tasks are time-consuming and do not always provide
correct or acceptable results.
this paper we have obtained relevant Big Data Visualization methods
classification and have suggested the modern tendency towards
visualization-based tools for business support and other significant
fields. Past and current states of data visualization were described and
supported by analysis of advantages and disadvantages. The approach of
utilizing VR, AR and MR for Big Data Visualization is presented and the
advantages, disadvantages and possible optimization strategies of those
visualization problems discussed in this work, it is critical to
understand the issues related to human perception and limited cognition.
Only after that, the field of design can provide more efficient and
useful ways to utilize Big Data. It can be concluded that data
visualization methodology may be improved by considering fundamental
cognitive psychological principles and by implementing most natural
interaction with visualized virtual objects. Moreover, extending it with
functions to exclude blind spots and decreased vision sectors would
highly improve recognition time for people with such a disease.
Furthermore, a step towards wireless solutions would extend device
battery life in addition to computation and quality improvements.
EO performed the primary literature review and analysis for this work
as well as designed illustrations. Manuscript was drafted by EO and AO.
EK introduced this topic to other authors and coordinate the work
process to complete the manuscript. EO, AO and TO worked together to
develop the article’s framework and focus. All authors read and approved
the final manuscript.
Compliance with ethical guidelines
Competing interests The authors EO, AO, EK and TO declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, and reproduction in any
medium, provided you give appropriate credit to the original author(s)
and the source, provide a link to the Creative Commons license, and
indicate if changes were made.
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