CASE STUDY ON METHODS AND TOOLS FOR THE BIG DATA ANALYSIS

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1707
Chand Babu1, Umesh Goyal2
1Research Scholar, Dept. of Computer Science & Engineering, FCEM, Faridabad, Haryana, India
2Assistant Professor, Dept. of Computer Science & Engineering, FCEM, Faridabad, Haryana, India
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Abstract - As the name of indicate, Big Data means huge
collection of data that can’t be proceed without traditional
computing approach. To Compute the data it’s need tool and
technique. Having data bigger consequentlyrequiresdifferent
approaches, techniques, tools & architectures to manage the
data in a better way. Big data technologies provide more
accurate analysis which help in decision making. To manage
and process huge volume of structured semi-structured and
unstructured data you would require an infrastructure that
can secure, privacy and protect the data. There are various
tools and technologies in the market from different vendors
IBM, Amazon, Microsoft, etc., to handle big data. The major
challenges with big data are Capturing data, Storing data,
Searching, Curation, Sharing, Transfer, Analysis, Presentation,
To fulfill the above challenges, organizations normally take
the help of enterprise servers.
diverse methodologies (technologies/methods) based on
certain parameters to chalk out which of them is best possible
(optimal) or what else needs to be done, to have an optimal
solution. As the data is being generated and accumulated at a
very high velocity with diversity in addition to it, processing
has become a tedious task. Whereas the fact remains that, by
processing of this data we will uncover gold from these huge
mountains but if it’s left untreated it will become Everest’s of
garbage. Since the foundation of term Big Data many
methodologies or technologies are present which are being
used to process these data mountains. Thesetechnologieshave
their own area of interest due to which there are obvious
drawbacks and mode of operations between them. After
analysis we found that common to them is low agility and the
Data management. Once the data Management issue of Cloud
technology is resolved it will prove a boon and cure to the
other drawbacks of cloud implementation for processing of
Big Data. It makes as sure that Cloud technology with better
Data Management will be implemented in every phase of life
from governments to business.
Key Words: Big Data, Big data tools and methods.
1. INTRODUCTION
Every second sees huge amounts of exponentially growing
data, being generated, acquired, stored, and analysed. The
revolution in the generation of massive data amounts
comes along with the Internet usage which allows data
exchange between various electronic devices and humans.
In this regard, the following fields are mentioned: mobile
phones, social media, imaging technologies to determine a
medical diagnosis, etc. The volume of available data
continues to grow and it grows in different formats.
Conversely, the price of data storagecontinues tofall which
results in data storing being more reachable. Although
creating data storage is getting cheaperandmoreavailable,
the increasing volume of data in different formatsandfrom
diverse sources creates new problems with regards todata
processing, including in its analysis and in integrating Big
Data into business decision making processes.
In order to store and process Big Data, new
technologies are evolving to address these problems. To
deal with these challenges, there is a need for a new
approach such as building a scalable and elastic
architecture. The purpose of this study is to explore the
domain of the Big Data problem; particularly, to create an
overview among free available repositories of biomedical
Big Data and to discover appropriate technologies and
methods along with their limitations and use cases to be
applied over the chosen data. Since the data, technologies
and methods are chosen, a testing scenario is created and
deployed over this data.
1.1 Big Data overview
There are many definitions for Big Data. Somebody
defines Big Data as data that is quite complicated rather
than “easy going” and it is hard to acquire, store,
manipulate and process it, due to the factitis“big”.Another
one, which is frequently mentioned is called“3V”described
by three words (Volume, Variety and Velocity). These two
definitions above and others are cited and mentioned
below:
“Big Data can be defined as volumes of data available in
varying degrees of complexity, generated at different
velocities and varying degrees of ambiguity, that cannot be
processed using traditional technologies, processing
methods, algorithms, or any commercial off-the-shelf
solutions.” [4]
Argues that there are three attributes standing out as
defining Big Data characteristics:
“Huge Volume of data: Rather than thousands or
millions of rows, Big Data can be billions of rows and
millions of columns.”
CASE STUDY ON METHODS AND TOOLS FOR THE BIG DATA ANALYSIS

“Complexity of data types and structures: Big Data
reflects the variety of new data sources, formats and
structures, including digital traces being left on the web
and other digital repositories for subsequent analysis.”
“Speed of new datacreationand growth:BigData can
describe high velocity data, with rapid data ingestion and
near real time analysis.”
The most known and used definition is originally
described as “3V” (see Figure ).Sincethenvariousauthors
have come up with other definitionsanddescriptions.The
combination of these thoughts is described below:
1.1.1 Volume – the volume is increasing exponentially.
There are many sources generating a huge amount
of data which takes a long time to process.
1.1.2 Variety – Big Data is not always structured data.
Actually, most parts of it are unstructured. In
addition, it comes in different formats due to the
variety of data sources; dealing with the data
formats variety, increases the complexity of storing
and analysing.
1.1.3 Velocity – describes the rate of data change. Data
volume changes dynamically. There is a need to
manipulate with data in real-time. However, for
some tasks in fields such as business and health-
care, it can be somehow demanding.
1.1.4 Value – it is often argued that the value is the most
critical part of Big Data. Most of projects have to
produce appropriate results, unless the
company/institution can waste financial sources. It
costs a lot of money to implement IT infrastructure
systems to store data. If the subject which stores
data is not able to extract data value, itisdesirableto
consider if the intention of the investment to an
implementation of IT infrastructure systems was
suitable.
1.1.5 Veracity – not all data has tobe perfectlygood,butit
does need to be almost good to give relevant sight.
Upon considering the errors rate and the data
incompleteness,ambiguity inthedatasetisdesirable
and even necessary for further data analysis.
Credibility is another aspect ofdata veracitywhich is
worth mentioning.
Figure 1: Big Data characteristic
1.2 Big Data evolution
To better understand what Big Data is and where it
comes from, it is crucial to first understand some past
history of data storage, repositories and tools to manage
them. As shown in Figure , there has beena hugeincreaseof
data volume during the last three decades.
As we can see in the decade of 1990s the data volume
was measured in terabytes. Relation databases and data
warehouses representing structured data in rows and
columns were the typical technologies to storeandmanage
enterprise information.
Subsequent decade data started dealing with different
kinds of data sources driven by productivity and publishing
tools such as content managed repositories and networked
attached storage systems. Consequently, the data volume
was started being measured in petabytes.
Figure 2: Data evolution and the rise of Big Data sources
1.3 Source of Data
Typically, the first type is bound up with the spread of
machines digitalisation which is related to sensors
integration, the connectivity increase, and devices
recording sounds, images or videos and to machines
communication between each other. Particularly, devices
such as cameras recording videos, cell phones collecting

geospatial data, machines in production lines of industrial
systems, are exchanging important information while
processing their activities. More examples are mentioned
below:
Medical information – machines recording EEG
(Electroencephalography), heartbeats, genomic
sequencing.
Multimedia – photos and videos uploaded on the
Internet,
Mobile devices – provide geospatial data (location,
gyroscope), as well as metadata about phone calls,
messages, internet usage and data gathered by mobile
applications,
Other devices – Warehouse Management Systems
(WMS) providing inside location realised by Wi-Fi,
identification of stored products or material by BAR, QR
(Quick Response) codes or RFID (Radio-Frequency
Identification) chips. There is a presence of many other
technologies such as navigation systems, seismic
processing, etc.
It is appropriate to consider other sources generatedby
activities on the Internet in general. Letusimaginehaving a
set of servers which run web pages that can be determined
for retail business. The servers can collect records of all
activities of the websites’ customers, users, transactions,
applications and servers own activity and behaviour. For
instance, there are logs which can be collected of [2].
1.4 Characteristics of Big Data
In relation to the definitions, the reason why there
is intense complexity in processing Big Data is shown.
Along with the Big Data there also exists ambiguity,
viscosity and virality (see Figure).
Ambiguity – emerges when there is less or no
metadata in Big Data. An example can be a graph or
something that usually needs a description. Letters M
and F in a graph can depict genders or they can even
represent Monday and Friday.
Viscosity – this term is often used to describe the
latency time in the data relative to the event of being
described.
Virality – describes how quickly data is shared
throughout a network among people who are
connected. The measurement result is the rate of
spread of data in time. For instance, Twitter can be a
relevant example when the tweets are spreadingfrom
the first (root one) original tweet among people
throughout the network.
Figure 3: Big Data characteristics derived from ‘3 V’
definition
1.5 Big Data vs Small Data
Big Data is not simply small data thathasgrown. There
are more aspects that define differences between these
two categories. A subset of the aspects can be derived
from the “3V” definition described above and the others
are argued by [6] (see Table 1).
SMALL DATA BIG DATA
GOALS
Usually designed to
answer a specific
question or to achieve a
particular goal
Nobody knows what the
exact output of the project is.
Usually it is designed with a
goal in mind, but the goal is
flexible
LOCATION
Typically, small data is
contained within one
institution, often on
one computer or even
in one file
Big data are located
throughout the company
network or throughout the
Internet. Typically, it is kept
onto multiple servers, which
can be everywhere
DATA STRUCTURE
Ordinarily contains
highly structured data.
Commonly, the data
domain is restricted to
a single discipline or its
sub-sequence. The
typical forms of its
storage are uniform
records or
spreadsheets
Has to be capable of
absorbing unstructured data
such as text documents,
images, sounds and physical
objects. The subject of
disciplines can vary
throughout the data
DATA PREPARATION

In many cases, the data
is prepared by its own
user for his own
purposes
The data is collected from
many different sources.
People who the data comes
from are rarely the same
people who use the data
LONGEVITY
The data is kept for a
limited time (academic
life). After few years
when the data project is
finished, the data is
usually discarded
A Big Data project typically
contains data which has to
be stored in perpetuity
MEASUREMENTS
Typically, the data is
measured using one
experimental protocol
Many various types of data
are measured in many
different electronic formats
REPRODUCTIBILITY
The projects are
typically repeatable. If
there is a problem with
the data quality, the
entire project can be
repeated
Replication of the project is
seldom feasible. There is
nothing more than optimism
that the bad quality data is
found and flagged, rather
than be replaced by a better
one
STAKES
Project costs are
limited. The institution
can usually recover
from small data failure
Big data projects can be
really expensive. A failed Big
Data project can lead to
bankruptcy
INTROSPECTION
Data points are
identified by rows and
columns within a
spreadsheet or a
database. It enables to
address a particular
data point
unambiguously
It is more difficult to access
the data. The organisation
and the context can be
inscrutable. Access to the
data is achieved by a special
technique referred to as
introspection
ANALYSIS
In most cases, all the
data in the project can
be analysed together
Big data is typically analysed
in incremental steps. It is
extracted, reviewed,
reduced, normalised,
transformed, visualised,
interpreted and reanalysed
with different methods
Additionally, in relationtothevolumeofBigData andits
incompleteness, corruption and ambiguity that are caused
by its large volume, the Small Data is generally more
organised, structured and it is also possible and desirable
to avoid the incompleteness together with the other
attributes of Big Data (see Figure ).
Figure 4: Small and Big Data illustration1
2. BIG DATA TOOLS
Although Big Data principles andapproachesarefrequently
discussed, there are not many technologies which are
convenient to deal with such data. Due to the definitions of
the volume and the velocity, the tools which are supposed
to deal with Big Data have to offer a distributed computing
approach. There are the following approaches: multiple
data and single program, and single data and multiple
program.
In the first case, there is a single program, which is run
on more nodes, where all nodes process different data. On
the contrary, the second case isconsideredtohaveonlyone
dataset, which is processed by a program divided on small
tasks that are run on different nodes in parallel.
Due to it, there are tools that try to abstract from the
physical distribution as much as possible. SincetheApache
company released its new implementation of Map Reduce
paradigm, a whole ecosystem called Hadoop has started
evolving. The MapReduce paradigm offers the means to
break a large task into smaller tasks, run in parallel, and
consolidate the outputs of the individual tasksintothefinal
output. The significant ecosystemexpansionwascaused by
using simpleprogrammingmodelstoprocesslargedatasets
across clusters as well as was amplified by the fact that the
whole solution has started as open source software.
Hadoop as the first publicly known and discussed
technology of Big Data processinghasbeenusedasthebase
of open source and commercial extensions. In otherwords,
most of the set of Big Data tools are based on the Hadoop
solution.
These solutions offer methods and approaches to load,
pre-process, store, query and analyse data.

In the following subchapterstheHadoopecosystemwill
be described along with other technologies which have
been evolved from it or others which are using its
technologies.
2.1 MapReduce
As mentioned earlier, the MapReduce paradigm
provides the means to break a large task into smallertasks,
run the tasks in parallel and consolidate the outputs of the
individual tasks into the final output. MapReduce consists
of two basic parts: a map step and a reduce step. [1]
• Map – performs an operation to a piece of data which
generates some intermediate output.
• Reduce – gathers the intermediate outputs from the
map steps, processes it and provides the collected final
output.
The main advantage of MapReduce is the workload
distribution over a cluster of computers (to run tasks in
parallel). Particularly, MapReduce provides a technique,
which allows the processing of one portion of the input
which can be run independently of the other input parts.In
other words, the workload can be easily distributed over
the cluster.
2.2 Distributed File System – HDFS
The Hadoop Distributed File System (HDFS) is a file
system which provides the capability to distribute data
across a cluster to take advantage oftheparallel processing
of MapReduce.
HDFS is designed to runoncommonlow-costhardware.
Consequently, it means there is no need todeployitonlyon
super computers. Although, itisimplementedinJava,HDFS
can be deployed on a wide range of machines apart from a
node, which is dedicated to manage namespace services
(see Architecture below).
2.2.1 Architecture
HDFS has a master/slave architecture as shown in
Figure . It consists of a single master server whichmanages
the filesystem namespace and manages access to files by
clients and a single NameNode. In addition, there are
DataNodes which are usually bound up with a node in the
cluster. These DataNodes manage storage within their
nodes that they run on. A file in HDFS is split into one or
more blocks that are stored by a set of DataNodes.
Moreover, they are responsible for serving read and write
requests from the file system clients and performing
blocks’ creations, deletions and replications which are
requested by NameNode. Whenever there is a request for
an operation as opening, closing, renaming of a file or a
folder, it is handled by the NameNode. Additionally, it is
also in charge of the blocks mapping to DataNodes. [11]
Figure 5: HDFS Architecture
2.2.2 File Storage
HDFS breaks files typically into64MBblocksandstores
the blocks throughout the cluster. Whenever possible,
HDFS attempts to store file blocks on different machinesto
allow the map step to operate on each block of a file in
parallel. If a file size is 200 MB, the file is stored in four
blocks: three 64 MB blocks and one 8 MB block. If the file is
smaller than 64 MB, the block represents the whole file.
HDFS is determined to deal with large files such as files of
up to 1 GB. Files with a size smaller than 64 MB are
considered as small sizes which are not efficiently
maintained by HDFS.
2.3 MapReduce Job in Hadoop
A typical MapReduce program in Java is composed of
three classes: the driver, mapper and reducer.
The driver – contains the job details and its configurations
such as input file locations, details for assigning the input
file to the map task, the names of the mapper and reducer
Java classes and it also contains the location of the reducer
task output.
The mapper – represents the logic to be processed on each
data block related to the defined input files in the driver
code.
The reducer – represents the logic of the gathering of
intermediate results from the mappers.
2.4 The Hadoop Ecosystem
As mentioned above, Hadoop evolution comes all along
with open source and commercial extensions to make
Apache Hadoop easier to use and provide additional
functionality and features. This subchapter examines the

following Hadoop-related Apache projects which all
together form the Hadoop Ecosystem.
2.5 Spark
It is an open source Big Data processing framework for
performing data analytics on a distributed computing
cluster such as Hadoop. Spark supports in-memory
processing to increase speed and data process over
MapReduce. It is discussed as a more powerful analysis
replacement of Hadoop. Spark is deployed on the top of
existing Hadoop cluster which enables Spark toaccessdata
via HDFS. Additionally, it can also process structured data
via Hive and stream data from HDFS. [13]
3. BIG DATA TOOL SELECTION
3.1 Storage
Nowadays, there are two databases where the data is
being stored within the virtual machine which has 100 GB
allocated. As result of the data analysis, without external
stimuli the data volume will probably reach the level of 1 TB
in approximately the year 2032. The current data storage
system is sufficient for next following years. Most of
PostgreSQL database size is occupied by binary files, which
cannot be queries. With this in mind, there is no need to
exchange this RDBMS for other more scalable technology
such as a distributed file system. Moreover, the metadata of
experiments are currently being stored in a NoSQL database
where it can be queried in a more efficient way.
3.2 Pre-processing and Analysis
As said before, the whole process of analysis is
performed either within Matlab along with EEGLAB or by
applications written in Java.
3.3 Hadoop Based Solution
Most of technologies providing a scalable solution
for dealing with Big Data are based on Hadoop. Due to the
fact that Hadoop is implemented in Java, it provides suitable
API which enables writing distributed Java programs based
on MapReduce paradigm.
Hadoop is built on its distributed file system HDFS. HDFS
solution is not efficient when the file system is supposed to
deal with a huge amount of small files where a small file is
the file which is smaller or almost equal to the size of block
(default 64 MB). With this in mind, we can state that this
solution could not be efficient in the domain of the EEG/ERP
because the size of the most of the data files is less than 27
MB in 63 % of cases. Moreover, the remaining rest 37% of
other files have different sizes, it means, whichmeans,thatif
it they were stored in HDFS, most of the files would not fill
HDFS blocks appropriately due to its inner fragmentation.
Although Hadoop provides some approaches how to deal
with small files, neither of them is convenient to be applied
in this context. The process of the data generation cannot be
changed because the generated files are associated with
particular experiments. Similarly, the same problem would
occur if we used the method of batch file consolidation.
Sequenced files technique, seems to be better, but if we
consider that we would like to run a methodoverthedataset
of one experiment, it would not be efficient either because
one map operation is run over onedata block,wherethefiles
of one dataset could be stored anywhere. In general, the
solutions based on Hadoop are not appropriate Big Data
technology for the domain of the department’s EEG/ERP
project. Moreover, there is no need to store data in a
distributed file system.
3. 4 MATLAB
Matlab provides a number of techniques and
approaches to handle Big Data. Althoughthere aretoolssuch
as memory mapped variables, disc variables and datastore
which enable to deal with problems that occur when a large
data has to be loaded into a computer’s memory at once,
they are considered as a method which provides a scalable
solution. This functionality is enabled by Parallel Computing
Toolbox along with Distributed Computing Server that
provide a scalable distributed solution.Thissolutionenables
to run methods for data pre- processingandanalysingovera
scalable Matlab cluster.
3.5 Evaluation
A Matlab cluster seems to be the best solution for the
application of a Big Data approach within the EEG/ERP
project. Moreover, the researchers are familiar with this
environment, which contains many available methods for
data pre- processing and analysis either _rogrammed as
Matlab scripts by the researchers or as functions provided
by EEGLAB plugin which is widely used among this
community.
4. LITERATURE REVIEW
The characteristics of Big Data were first described in 2001,
when Laney [4] identified three key attributes of large data
amounts: high variety, volume, and velocity. To date, these
attributes have become the defining characteristics of Big
Data. However, contemporary authors and business
specialists enlarged these defining characteristics with
further aspects such as dedicated storage, management,and
analysis techniques [8], [9],[10].Furtheramendmentsto the
definition include the addition of a fourth V, veracity,byIBM
[11], emphasizing the aspect of data quality. Taking these
different extensions of the original definition into account,
we define Big Data as a phenomenon characterized by an
ongoing increase in volume, variety, velocity, andveracityof

data that requires advanced techniques and technologies to
capture, store, distribute, manage, and analyze these data.
Mark Beyer, Douglas Laney Published on 21 June 2012"Big
data" warrants innovative processing solutions for a variety
of new and existing data to provide real business benefits.
But processing large volumes or wide varieties of data
remains merely a technological solution unless it is tied to
business goals and objectives.
Ming Ke, Yuxin Shi published on September 17, 2014 “Big
Data, Big Change: In the Financial Management” In recent
years, "Big Data" has attracted increasing attention. It has
already proved its importance and value in several areas,
such as aerospace research, biomedicine, and so on. In "Big
Data" era, financial work which is dominated by transaction,
business record, business accounting and predictions may
spring to life. This paper makes an analysis about what
change that "Big Data" brings to AccountingData Processing,
Comprehensive Budget Management, and Management
Accounting through affecting the idea, function, mode, and
method of financial management. Then the paper states the
challenges that "Big Data" brings to enterprise aiming to
illustrate that only through fostering strengths and
circumventing weaknesses can an enterprise remain
invincible in "Big Data" era.
Aicha Ben Salem, Faouzi Boufares, Sebastiao Correia
published on April 2014 “Semantic Recognition of a Data
Structure in Big-Data” In fact, good governance data allows
improved interactions between employees of one or more
organizations. Data quality represents a great challenge
because the cost of non-quality can be very high. Therefore
the use of data quality becomes an absolute necessity within
an organization. To improve the data quality in a Big-Data
source, our purpose is to add semanticstodata andhelpuser
to recognize the Big-Data schema. The originality of this
approach lies in the semantic aspect it offers. It detects
issues in data and proposes a data schema by applying a se-
mantic data profiling
5. CONCLUSIONS
Each second sees a huge amount of data being generated
either by human collaboration or by machines which are all
around us. The Age of Big Data has come and there is a need
to address the challenges which come along with it.
Consequently, the problem of Big Data is widely discussed;
many books and journals have been published to addressits
challenges, definitions and recommendationsonhowtodeal
with it. Moreover, the terms such as privacy, security and
ethical problems are also considered.
Although Big Data is a frequently discussed topic in
theoretical manners, there is deficiency in publications and
sources dedicated to its practical usage. Nevertheless, there
are some evolving technologies such as Apache Hadoop
along with its ecosystem. This technology is considered as
the first open-source and widely used Big Data technology,
building upon a distributed filesystem and an
implementation of MapReduce paradigm. Most of the other
technologies dedicated to deal with Big Data are based on
the Hadoop solution. Although Hadoop is often discussed as
a universal Big Data platform, it cannot address all Big Data
problems. There are still some available solutions which are
not based on Hadoop such as Matlab, a system which
provides a different approach of a cluster computation run
over a shared filesystem.
Furthermore, it is convenient to mention where Big Data is
stored and how much it is available. Although there are
many possibilities on how to access Big Data of various
types, biomedical data is mostly not much available.There is
only one publicly opened biomedical Big Data database
which is intended to preserve genomes. On the other hand,
due to its big volume, there is considerable quality variety
among publications where the database is described. Other
available databases cannot compete with the volume of the
genome database. However, they are still characterised by
some qualities which should be considered. (consider
revising sentence to make it clearer) Nevertheless, dueto its
domain, availability, many publications and information
within the project, the database of the EEG/ERP project was
evaluated as the most suitable one.
Depending on the EEG data characteristics, which were
obtained by the deep database analysis, Matlab solutionwas
evaluated as suitable technologyforapplicationonEEGdata.
Additionally, Matlab is widely used within the EEG project
for the data processing and its programs can be deployed
over a Matlab cluster. Although the Computer Science and
Engineering Department does not possess all required
licences, there is the possibility to use the project
Metacentrum where all licences are available along with
countless hardware resources.
To confirm that a Matlab cluster is a solutionwhichcanbe an
asset for the EEG/ERP project, a model which allows the
running of a Matlab program over multiple data on a Matlab
cluster was created. The model was tested by performing
two use case, where EEG signals were either divided on
epochs or filtered over two experimentdatasets.Thistesting
has shown that the model is functional and can be
considered as an asset fortheEEG/ERPproject.Additionally,
a few recommendations are proposed on how the project
can be improved further.
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