Big continually generating the large size, different variety

Big Data

Haroon
Sarwar, University of Bradford

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Abstract

In this era,
the internet application and communication have seen a lot of expansion and
reputation in the field of Information Technology. These internet applications
and communication are continually generating the large size, different variety
and with some genuine difficult multifaceted structure data called big data. Currently,
new sources of data emerges that hold the potential to transform how
organisations drive, whether it be an advantage or a disadvantage to them. Today,
we are in the midst of yet another data-driven business revolution. New sources
of social media, mobile and sensor or machine-generated data hold the potential
to rewire an organizations value creation process.

Keywords

Big Data, Analytics, Cloud
Computing, Data Mining

1.0 Introduction

Big
Data is becoming one of the most important technology trends that has the
potential for dramatically changing the way organisations use information to
enhance the customer experience and transform their business model. Big data is
often characterized by the “3Vs” which consists of Volume, Velocity, and
Variety. Although big data doesn’t equate to any specific volume of data, the
term is often used to describe Terabytes, Petabytes, and even Exabyte of data captured
over time.  

1.1 Literal Definition

The term
Big Data is recognised for database systems. It is used for a number of technologies,
which help to organize data. It is mainly used in the field that studies
management and processing of any dataset too large for direct and individual
interpretation.  Oftentimes, storing
large quantities of data, introduces mechanical problems. The disk drives and
RAM cannot contain it all in one processer, so the databases have to be set in
to different parts and distributed across a number of systems. “This big data
is not only generated by traditional information exchange and software, but
also from sensors of various types embedded in a variety of environments;
hospitals, metro stations, markets, and virtually every electrical device that
produces data.” (Mediratta, 2015)

1.2 Why is Big Data Important?

“Big Data
may open up radical new ways and unprecedented opportunities of attacking software
engineering problems. Already now forums, forges, blogs, Q&A sites, and
social networks, provide a wealth of data that may be analysed to uncover new
requirements, provide evidence on usage and development trends of application
frameworks, or to perform empirical studies involving real-world software
developers”(Baresi, L 2016). The
importance of big data does not revolve around how much data you have, but what
you do with it. A person or organisation can take data from any source and
analyse it to find answers that enable cost reductions, time reductions, new
product development and optimized offerings, and smart decision-making. When
you combine big data with high-powered analytics, you can accomplish
business-related tasks such as:

·        
determining
root causes of failures, issues and defects in near-real time,

·        
recalculating
entire risk portfolios in minutes,

·        
In
addition, detecting fraudulent behaviour before it affects a person or an
organisation.

 

1.3 Big Data Analytics

Big Data Analytics
refers to the analysis through inspection, cleaning, transformation, models and
verification working towards the creation of conclusions and decision making on
the true meaning of the data. Data analytics examines large amount of data to
uncover hidden patterns, correlations and other insights. With today’s
advancement in technology, it is possible to analyse data and get answers from
it almost immediately – an effort that is slower and less efficient with more
traditional business intelligence solutions.

1.4 Storage System

The rapid
growth of data has restricted the capability of existing storage technologies
to store and manage data. Over the past few years, traditional storage systems
have been utilized to store data through structured Relational Database
Management Systems (RDBMS). However, Storage systems have limitations and are
inapplicable to the storage and management of big data. A storage architecture
that can be accessed in a highly efficient manner while achieving availability
and reliability is required to store and manage large datasets.

1.5 Data Management

One of the
most time-consuming and labour-intensive tasks of analytics is preparation of
data for analysis; a problem often exacerbated by Big Data as it stretches existing
infrastructure to its limits. Performing analytics on large volumes of data
requires efficient methods to store, filter, transform, and retrieve the data. Processing
big data involves the use of unconventional data management, data
representation and data compression methods for unstructured data. This in
turn, requires new database systems that are better suited for this kind of
data sets. Big data does not have well defined data models and schemas as in
relational database systems.

1.6 Data Mining

Data
mining involves exploring and analysing large amounts of data to find patterns
for big data. Data mining software is one of a number of analytical tools for analysing
data. It allows users to analyse data from many different dimensions or angles,
categorise it, and summarize the relationships identified. It relies heavily on
algorithms and statistical methods to uncover patterns and create models of the
data.  Technically, data mining is the
process of finding correlations or patterns among dozens of fields in large
relational databases.

2.0 Cloud Computing

Cloud
computing is a powerful technology to perform massive-scale and complex
computing. It eliminates the need to maintain expensive computing hardware,
dedicated space, and software. A fast-growing technology, which has established
itself in the next generation of the IT industry and business. Cloud services
have become a powerful architecture to perform complex large-scale computing
tasks and span of IT functions from storage and computation to database and
application services. The need to store, process and analyse large amount of
datasets has driven many organizations and individuals to adopt cloud
computing.  The national institute of
standards and technology (NIST) define cloud computing as: “a model for
enabling ubiquitous, convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, servers, storage,
applications, and services) that can be rapidly provisioned and released with
minimal management effort or service provider interaction. This cloud model is
composed of five essential characteristics, three service models, and four
deployment models”. (Mell, P.  2011)

2.1 Mobile Cloud Computing

The increasing
popularity of wireless networks and mobile devices has taken cloud computing to
new heights because of the limited processing capability, storage capacity, and
battery lifetime of each device. This condition has led to the emergence of a
mobile cloud-computing paradigm. Mobile cloud facilities allow users to
outsource tasks to external service providers, for example, data can be
processed and stored out of a mobile device. Mobile cloud applications, such as
Gmail, iCloud, and Dropbox, have become prevalent recently. “The main objective
of cloud computing is to use huge computing and storage resources under
concentrated management, so as to provide big data applications with
finegrained computing capacity” (Min, C., et al 2014)

2.2 Relationship between Cloud
Computing and Big Data

Cloud computing
and big data are conjoined. Big data provides users the ability to use
commodity computing to process distributed queries across multiple datasets and
return resultant sets in a timely manner. Cloud computing provides the
underlying engine through the use of Hadoop, a class of distributed
data-processing platforms.  

Big data
utilizes distributed storage technology based on cloud computing rather than
local storage attached to a computer or electronic device. Big data evaluation
is driven by fast-growing cloud-based applications developed using virtualized
technologies. Therefore, cloud computing not only provides facilities for the
computation and processing of big data but also serves as a service model.

2.3 Hadoop

Hadoop is
an open-source software framework for storing data and running applications on
clusters of commodity hardware. It provides massive storage for any kind of
data, enormous processing power and the ability to handle virtually limitless
concurrent tasks or jobs. Hadoop makes it possible to run applications on
systems with thousands of commodity hardware nodes, and to handle thousands of
terabytes of data. The distributed file system facilitates rapid data transfer
rates among nodes and allows the system to continue operating in case of a node
failure.

3.0 Case Studies

The relationship
between Big data and Cloud computing is contemplated by reported case studies
on big data using cloud computing technology.

3.1 Nokia

Nokia is a
mobile communications company whose products comes to be an integral part of
people’s lives. Many people around the world use Nokia mobile phones to
communicate, capture photos and share experiences. Thus, Nokia gathers and analyses
large amounts of data from mobile phones. However, in order to support its
extensive use of big data. Nokia relies on a technology ecosystem that includes
a Teradata Enterprise Data Warehouse, numerous Oracle and MySQL data marts, visualization
technologies, and Hadoop. Nokia has over 100 Terabytes of structured data on
Teradata and petabytes of multistructured data on the Hadoop Distributed File System
(HDFS). The HDFS data warehouse allows the storage of all data and offers data
processing at the petabyte scale.

3.2 Google

Google constantly
develops new products and services that have data algorithms. Google uses big
data to refine its core search and ad serving algorithms. Although these days
Google’s big data innovation goes well beyond basic search, it’s still their
core business. They process 3.5 billion requests per day and each request a
database of 20 billion web pages. This is refreshed daily, as Google’s bots
crawl the web, copying down what they see and taking it back to be stored in
Google’s index database. Google describes that the self-driving car is a big
data application. Using and generating massive amounts of data from sensors,
cameras, tracking devices and coupling this with on-board and real time data
analysis from Google Maps, Street-view and other sources allows the Google car
to safely drive on the roads without any input from a human driver.  

3.3 KIA

The car
manufacturer Kia Motors is always eager to find out what customers think and
say about their cars. The company uses sentiment analytics tools to detect what
is said about the brand and products on various blogs, Twitter and Facebook.
For example: Big Data technology has enabled the marketing team to examine the
power of an advertisement spot during the Super Bowl game, based on the
reactions on social media platforms.

3.4 Etihad Airways

Etihad
Airways, which carries over 10 million passengers annually is using Big Data to
optimize their pricing strategy. They have achieved this by making use of their
frequent flying programme to develop strategic pricing. By examining the
frequent flying behaviour, the company can improve pricing by tracking upgrade
frequency and other transactions.

 

4.0 Conclusion

Big data
is a broad, rapidly evolving topic. While it is not well-suited for all types
of computing, many organizations are turning to big data for certain types of
work loads and using it to supplement their existing analysis and business
tools. Big data systems are uniquely suited for surfacing difficult-to-detect
patterns and providing insight into behaviours that are impossible to find
through conventional means. By correctly implement systems that deal with big
data, organizations can gain incredible value from data that is already
available. Although this paper clearly has not resolved the entire subject
about this substantial topic, hopefully it has provided some useful discussion and
a framework for researchers.  

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