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Big Data Paradigm- Analysis, Application, and Challenges
U. Z. EDOSIO
School of Engineering, Design and Technology
University of Bradford
Abstract- This era unlike any other is faced with
explosive growth in the sizes of data generated. Data
generation underwent a sort of renaissance, driven
primarily by the ubiquity of the internet and ever cheaper
computing power, forming an internet economy which in
turn fed an explosive growth in the size of data generated
globally. Recent studies have shown that about 2.5 Exabyte
of data is generated daily, and researchers forecast an
exponential growth in the near future.
This has led to a paradigm shift as businesses and
governments no longer view data as the byproduct of their
activities, but as their biggest asset providing key insights
to the needs of their stakeholders as well as their
effectiveness in meeting those needs. Their biggest
challenge however is how to make sense of the deluge of
This paper presents an overview of the unique features
that differentiate big data from traditional datasets. In
addition, we discuss the use of Hadoop and MapReduce
algorithm, in analyzing big data. We further discuss the
current application of Big Data. Finally, we discuss the
current challenges facing the paradigm and propose
possibilities of its analysis in the future.
Keywords: Big Data, Large Dataset, Hadoop, Data
Over the last two decades of digitization, the ability of
the world to generate and exchange information across
networks has increased from 0.3 Exabyte in 1986 (20 %
digitized) to 65 Exabyte’s in 2007 (99.9 % digitized)
In 2012 alone, Google recorded a total of 2,000,000
searches in one minute, Facebook users generated over
700,000 contents, and over 100,000 tweets are
generated per minute on Twitter.
In addition, data is being generated (per second)
through: telecommunication, CCTV surveillance
cameras, and the “Internet of things” .
However, the “Big Data” paradigm is not merely
about the increasing volume of data but how to derive
business insight from this data. With the advent of Big
data technologies like Hadoop and existing models like
Clustering algorithm and MapReduce there is much
promises for effectively analyzing big data sets.
Notwithstanding, there are a lot of challenges associated
with Big Data analysis, such as: Noise accumulation and
highly probabilistic outputs; Data privacy issues and
need for very expensive infrastructure to manage Big
data . Fig 1 Illustrates the exponential growth of data
between 1986 and 2007.
Fig.1. Data growth between 1986 and 2007
II. DEFINITION OF BIG DATA
Currently, there is no single unified definition of “Big
Data", various researchers define the term either based
on analytical approaches, or its characteristics. For
instance; according to Leadership Council for
Information Advantage, Big Data is summation of
infinite datasets (comprising of mainly unstructured
data) . This definition focuses solely on the
size/volume of data. Volume is only one feature of Big
Data, and this does not uniquely differentiate Big Data
from any other dataset. On the other hand, some
researchers define Big Data in terms of 3 characteristics,
volume, velocity and variety also known as the 3 V
’s.–Variety depicts its heterogeneous nature (
comprising of both structured and unstructured
datasets), velocity represent the pace to which data is
acquired, and volume illustrates the size of data(usually
in Exabyte , Petabytes and Terabytes). This is a holistic
definition of the term Big Data; as it encapsulates key
features that uniquely identify Big Data and it opposes
the common notion that Big Data is merely about data
size. Fig. 2 illustrates the “3 V” characteristics of
Big Data, and also highlights how these characteristics
uniquely identify Big Data.
Recently, some researchers have proposed another V
–“Veracity” as a characteristic of Big Data. Veracity
deals with the accuracy and authenticity of Big Data. In
this paper we will focus solely on the volume, velocity
and velocity as this is more widely accepted
Analysis in Big Data refers to the process of making
sense of data captured . In order terms it can be seen
as systematic interpretation of Big Data sets to provide
insights or business intelligence which can foster
III. CHARACTERISTICS OF BIG DATA
This section explains each of the 3v characteristics of
A. Big Data Volume
According to , in 2012, it was estimated that about
2.5 Exabyte of data was created. Researchers have
further forecasted that, these estimates will double
every 40 months.
Various key events and trends have contributed
tremendously to the continuous raise in the volume of
data. Some of these trends include:
1) Social Media: There has been significant growth
in the amount of data generated from social media sites
.For instance, an average Facebook user creates over 90
contents in a month. Also, each day there are about 35
million status updates on Facebook.This is just a small
picture considering that there are hundreds of social
media application fostering users interaction and
content sharing daily . Fig. 3 provides more
insight on the amount of data generated from social
media sites in 60 seconds.
2) Growth of Transactional Databases:
Businesses are aggressively capturing customer
related information, in order to analyze consumer
behavior patterns .
3) Increase in Multimedia Content: Currently
multimedia data accounts for more than half of all
internet traffic. According to the Internet Data
Corporation the number of multimedia content grew
by 70% in the year 2013.
B. Big Data Variety
This characteristic is also referred to as the
Heterogeneity of Big Data. Big Data is usually
obtained from diverse sources, with different data
type, such as: DNA sequences, Google searchers,
Facebook messages, traffic information, weather
forecast amongst others.
Fig. 2. Characteristics of Big Data 
Specifically these data can be generically
categorized into structured, unstructured, semi-
structured, and mixed . This data is obtained from
wide variety of sample sizes (cutting across age,
geographical location, gender, religion, academic
C. Big Data Velocity
This refers to the speed at which data is generated
. Data can be captured either real time, in batches
or at intervals. For instance web analytics sensors
usually capture number of clicks on a website, (by
utilizing specialized programming functions which
listening to click event) on real time basis. For every
click, the web sensor analytic is updated immediately
(real time). However, in some cases data is captured
in batches, an instance of this is- Bank daily
transaction data- this is reviewed in batches at the
end of each day.
Big Data analytic systems unlike traditional
database management systems have the capacity of
analyzing data in real time to provide necessary
Fig. 3. Sources of Big Data 
Fig. 4: Technologies for Big Data Analytics 
IV. TECHNOLOGIES FOR ANALYSISING AND
MANAGING BIG DATA
Due to the 3v properties mentioned above, it is
impossible to process, store and analyze Big Data using
traditional relational database (RDBMS). However,
there are myriads of individual technologies and
libraries which provide an overall Big Data analytics
framework (when combined together). Fig 4 (above)
highlights each of these technologies and how they fit
into the overall framework. However, this report focus
only on the Hadoop architecture
A. Hadoop Architecture
Hadoop is an open source software built by Apache.
The software provides a platform for managing large
dataset, with 3V characteristics. In order words it
allows for effective and efficient management of Big
Hadoop consists of a data management system,
referred to as the Hadoop distributed storage file system
(HDFS) - this is a distributed file system that processes
and stores Big Data sets.
The HDFS is responsible for dividing Big Data (both
structured and unstructured data) into smaller data
blocks. After which the HDFS performs the following
on the data chunks:
1) Creates replicas of the smaller data: HDFS has
an inbuilt fault tolerance function, which creates
replicas of data, and distributes it cross various data
blocks (this good incase of disaster recovery) .
2) Distribute smaller data sets to slave nodes: Each
of these smaller data blocks are spread across the
distributed system, to slave nodes.
3) MapReduce: using a special functions/task
referred to Map() and Reduce() respectively, the HDFS
can effectively process this data, for effective analysis
B. MapReduce in Hadoop
MapReduce is a programming model integrated into
Hadoop, which allows for parallel processing of
individual data blocks or chunks in the HDFS. This
model comprises a Map() task - which is responsible
for breaking data chunks into more granular forms for
easy analysis. While the Reduce() task is responsible
for fetching the outputs of the map task and aggregating
it into a more concise format, which is easy to
understand. A function called Maplogik connect
enables storage of final output from the Reduce task to
the HDFS . All tasks on a single node are
managed by a Task Tracker, and all Task Trackers in a
cluster are managed by a single Job Tracker. Fig. 5.
presents a diagram illustrating mapping of data using
the Map task, and Reduce function to obtain a more
MapReduce can be used in a scenario where one
needs to obtain the number of word count in a
document. The pseudo-code is illustrated in fig. 6.:
Fig. 5: Illustration of Map and Reduce task in Hadoop 
Fig. 6: MapReduce Algorithm 
•Disk-Based Graph processing
•Hadoop Distributed File System
•MLPACK,Machine Learning Algorithm
•Mahout, Clustering Algorithm
V. APPLICATIONS OF BIG DATA
A. Business Intelligence
Based on a survey carried out by MIT Sloan
Management Research, in collaboration with IBM, on
over 3000 top management employees, it was
discovered that top performing organizations adopt Big
Data analytics five times more in their activities and
strategies than any other . Many organizations are
increasingly adopting analytics in order to make more
informed decisions and manage organizational risks
With the help of analytical tools like Hive, JAQL and
Pig integrated with Hadoop HDFS, business can
visualize (pictorially and diagrammatically) key insight
from Big Data .
For example: Many organizations currently analyze
unstructured data such as social feeds, tweets, chats,
alongside structured data such as stocks ,exchange
rates, trade derivative, transactional data in order to
gain understanding and insight on consumers
perception on their brands. By Analyzing these
voluminous data, in real time, organizations can make
calculated strategic decisions, in advertisement and
marketing, portfolio, risk management amongst
B. Predictive Analytics in Ecommerce, Health
The major principle applied in predictive analysis is
studying of words accumulated from various sources,
such as tweets, RSS feed(Big Data).
In 2008, Google was able to successful predict trends
in swine flu outbreak based on analysis of searches
only. This prediction was about two weeks earlier than
the U.S. Center of Disease Control. With such vast
sample size of real time data available, manual
statistical models are quickly becoming a thing of the
Majority of the online stores are taking advantage of
words tracking, to provide smart advertisements to
customers. For example Amazon and E-bay analyze
user online searches, by depositing cookies on browsers
in order to study consumers’ habit on the web. Based
on results from this analysis, they can provide
customized offers, discounts and advertisements based.
Predictive analysis promises to provide competitive
advantages to businesses by preempting “what if”
C. E-Government and E-Politics
Online campaign was first used in the United States
of America in 2008. These campaigns brought about
great success and participation by the general public.
Politicians used the web to publicize their policies,
events, discussions, and donations. These campaigns
were rich in multimedia contents and interactive with
the general public .
As e-government is gaining more grounds, politicians
are taking advantage of Big Data analytics, together
with business intelligence tools to analysis voter’s
perception. They are also democratizing campaigns
based on different audiences. This is made possible by
adopting clustering algorithm and HDFS system to
cluster different set of audiences. Other applications of
Big Data in politics include opinion mining, social
VI. CHALLENGES OF BIG DATA ANALYSIS
A. Data Privacy
Privacy is one of the major challenges facing Big
Data paradigm. Majority of people are concerned about
how their personal information is utilized. Big Data
analytics tends to infringe on our daily lives taking
advantage of the ubiquity of the internet. For instance,
Big Data analysis may involve studying consumer’s
social interactions, shopping patterns, location tracking,
communication and even innocuous activities like
power usage at one’s home. A commutation of all these
data can uniquely identify an individual and serve as
what is known as a “digital DNA”.
There major underlying data privacy challenges that
have plagued the Big Data Analysis paradigm. These
issues are not new to just Big Data but almost all ICT
It is involves issues pertaining to confidentiality
(who owns data generated?, who owns the results from
the analytic of the data?), integrity( Who vouches the
accuracy of the data?, who would take responsibility of
false positive data analysis?) , interoperability (who
stipulates the standards for data exchange?)and
availability  .
B. Noise Accumulation
Due to the heterogeneous nature of Big Data,
statistician and scientist predict high noise
accumulation in the Big Data sets. This feature is
peculiar to datasets which are varied and voluminous
from wide sample sources.
In the case of Big Data, majority of the data acquired
are mainly based on estimations, incomplete and
probabilistic in nature. For instance, imagine analysis of
consumer behavior; however in this instance the study
is based on a public system in a library. The data
generated from such analysis will be highly dispersed
as different individuals use that computer in their own
fashion. Imagine that a Big Data sample set contained
probably 50% of such noisy data. The resulting data
will be incoherent. Hence, results generated from Big
Data are not always truthful , since data is generated is
highly prone to error.
In response to this challenge scientist are adapting
machine learning to analyze Big Data. However this is
impractical as the machine learning algorithms expect
only homogenous input data.
C. Cost of Infrastructure
Over the past decades the data generation has out
grown computer resources. Many computer
manufacturers are constantly producing systems with
higher processing powers and Hard disk space .
Currently, the traditional hard disk storage system is
been replaced with solid disk drive state.
However, in the case of Big data due to the volume
and the velocity of data generated per time, a more
stable computer processor will be need in futures. Also
with the advent of a new paradigm referred to as the
“internet of things” there will be more data generated
into the internet (data generated from objects and
human, or object to object interaction). This would
require large data ware houses.
These resources are very expensive to install and
manage. Many organizations may find it too expensive
to acquire and may choose to stick with the traditional
means of data analysis.
Although, the analytic framework for Big Data is not
100% accurate many organizations, are applying the
technology regardless. According to Mckinsey in ,
majority of the top five business organizations in the
USA claim to yielding tremendous growth.
Currently, statisticians and computer scientist are still
searching for better statistical models, and algorithms to
fine tune the noisy results and produce more precise
and accurate insight from big data analysis.
However, I recommend there is need for more urgent
research on stable hard ware systems and computational
algorithms to manage and produce insights at optimum.
As data growth is a going concern.
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