Cloud Computing and Big Data Applications

This document analyzes various research papers focusing on the intersection of cloud computing and big data. It examines different analysis methods employed in these papers, including qualitative and quantitative approaches. The papers delve into topics such as web analytics, distance learning, note-taking applications, and the features and challenges of big data within cloud environments. The assignment also highlights the potential benefits and limitations of cloud computing and big data implementation across diverse sectors.

Running head: CLOUD COMPUTING
Cloud Computing
Name of the Student
Name of the University
Author’s note

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Table of Contents
Summary of Paper 1: Implementation of Cloud Computing and Big Data with Java Based
Application......................................................................................................................................2
Summary of Paper 2: Amalgamation of Web Analytics with Cloud Computing...........................2
Summary of Paper 3: Web Service Model for Distance Learning Using Cloud Computing
Technologies....................................................................................................................................3
Summary of Paper 4: Implementing a cloud backed scalable note-taking application with
encrypted offline storage and cross platform replication................................................................4
Summary of Paper 5: Big Data in Cloud Computing: features and issues......................................5
Comparison Table............................................................................................................................6
References........................................................................................................................................8

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Summary of Paper 1: Implementation of Cloud Computing and Big Data with Java Based
Application
Big data along with cloud computing based applications are developed in Java. There are
three types of cloud computing deployment models: public, private and hybrid cloud. In this
paper, the SPI models called SaaS, PaaS and IaaS models are also discussed. Resource pooling,
virtualization, accessibility, on-demand service and scalability are the characteristics of cloud
computing.
Big data is defined as a high volume and high speed data. The applications of big data
include fraud detection, social media analytics, IT log and call center analytics. Big data can be
used in various industries like financial sector, insurance organizations and retail associations.
Hospitals make use of big data to investigate medicinal information as well as patient records.
Implementing cloud computing with java framework will help to improve the efficiency
of execution by distributing the application execution at various levels. Implementation of big
data on cloud over java framework will help to solve real life issues like human resource and
traffic management [1]. Distributed parallel processing performs eight times better statistical
analysis than any other distributed batch procedures. This prototype demonstrates how it can be
used for delivering correct product to customer at a shortest possible time.
Summary of Paper 2: Amalgamation of Web Analytics with Cloud Computing
E-commerce businesses will be able to gain profit and reduce cost by amalgamating web
analytic with cloud. It will enable the businesses to access useful information from virtual data
warehouses without spending huge cost of storage and infrastructure. Cloud computing has

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service models like PaaS, SaaS and IaaS. Web analytics can be considered to be an essential tool
for business syndicates for generating high profits.
Web analytics is a complex procedure the takes data input and processes the data to
deliver the output [2]. PRM model can be used for optimizing and measuring success metrics at
each step of the web merchandizing. There are three components in this model called:
performance breakdown, resultant and maturity breakdown. The performance breakdown
component is used for collecting data by using analytics tool. The maturity breakdown
component analyzes the competence of websites.
The principles of web analytics can be used in cloud drives for amalgamation of cloud
and web analytics. Analytics will enable the organizations to recognize changing climates and
take appropriate steps for staying competitive in the market. Consolidation of analytics and cloud
will increase the effectiveness of the businesses by enabling the companies to store, interpret as
well as process big data for meeting their requirements.
Summary of Paper 3: Web Service Model for Distance Learning Using Cloud Computing
Technologies
Web services using cloud technologies can be used for exchanging resources between
students and teachers for improving the learning procedure. One major benefit of cloud
technology is providing new opportunities in economics, business and education. Exchange of
collaborative documents and information has increased the flexibility. The execution of tasks has
become faster.
The logical architecture of web service integrates OpenLDAP, MySQL databases and
Moodle LMS. The web applications that are developed for various platforms would be

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dependent on cloud infrastructure and must be integrated with the LDAP. The application would
be based on service-oriented architecture. Web service is known for integrating business logic
and the system.
An experiment has been set up to find out how cloud based e-learning affects the results
of the students. This experiment also finds out the satisfaction level of the teachers. A tool called
Ganglia is used for determining the performance of cloud based e- learning system. This
software tool helped to gain insight into deeper analysis like system stability and availability [3].
Cloud infrastructure improves the productivity of the teachers by enabling them to prepare
courses and in identity management. The result of the experiment showed that cloud based
online education gives better performance and is more scalable.
Summary of Paper 4: Implementing a cloud backed scalable note-taking application with
encrypted offline storage and cross platform replication
An application called “Bluejot” was designed and developed. The most important aspect
of the note-taking app is the note content. The main objective of the app is to allow users to
reliably and quickly take notes. The note content of the Bluejot app takes centre stage without
any clutter.
A responsive design of a web app is considered to be one of the most important design
considerations of the note-taking app. The application will be user interactive that will make user
experience smooth and simple. The notes will be synched across different devices used by the
users and will be saved when working offline.
A cloudant solution can be used for storage of the notes in the cloud environment. The
requirements of the cloud architecture are: scalability, portability, deployment ease and

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availability. There are two interfaces: one API used by the mobile clients and web interface for
the desktop users. There are vertical and horizontal scaling of the Bluejot app. Docker platform
allows continuous integration and streamlined deployment [5]. This Bluejot app solution
presented in this paper differentiates itself from the other apps by a contained and secure user
experience that prevents data leakage.
Summary of Paper 5: Big Data in Cloud Computing: features and issues
Big data in cloud computing has like security issues, data privacy, data heterogeneity,
recovery techniques and cloud data uploading techniques. Nokia and RedBus are two such
examples that demonstrate how big data and cloud computing can work together.
There are five aspects of big data are variety, volume, velocity, veracity and value. It
also describes the three basic service models of clouds: IaaS, PaaS and SaaS. Cloud computing
and big data are compatible concepts as the cloud allows the big data to be scalable, fault tolerant
and scalable. Business considers big data to be a valuable opportunity for business.
Nokia considers Big Data as a Service (BDaaS) to be advantageous. Nokia trusted
Cloudera for deploying Hadoop environment. RedBus initially used Hadoop servers for data
processing [4]. Later on RedBus used Google bigQuery to fulfill their requirements and achieve
capabilities of analyzing real-time data at a cost which is 20 % less than the cost of maintaining
Hadoop infrastructure. There are certain big data issues like privacy, security, heterogeneity and
data governance.

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Comparison Table
Sr.
No.
Paper Methodology Feature Set Advantages Limitations
1 Implementation of Cloud
Computing and Big Data
with Java Based
Application [1]
Secondary Analysis
method
Types of cloud,
concept of cloud, and
use of big data in real
world
This paper also
focuses on the concept
of big data in cloud.
In future, the author aimed to
develop cloud computing and
big data with struts, spring, and
hibernate integration for
reducing cost on infrastructure
over java-based applications.
2 Amalgamation of Web
Analytics with Cloud
Computing [2]
Qualitative method of
analysis
This paper describes
the concept of IaaS,
PaaS and SaaS.
The use of analytics
and cloud computing
will generate high
profit for a business.
The primary limitation exists
for utilizing concept over
analytics; as it can provide
theoretical implications without
practical outcomes.
3 Web Service Model for
Distance Learning Using
Cloud Computing
This paper has used
quantitative or primary
analysis method where
This particular paper
focuses on how cloud
computing based e-
This particular paper
points out how cloud
will benefit the
Primary limitation in this paper
is that some teachers are not
satisfied with technical support

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Technologies [3] a survey was conducted
to collect data.
learning will improve
the results of students.
society. provided from the quality.
4 Implementing a cloud
backed scalable note-taking
application with encrypted
offline storage and cross
platform replication [4]
Secondary analysis
method
This particular paper
presents an idea about
note-taking
application.
The primary
advantage of the paper
is that it provides an
application of cloud
computing with
scalability.
Note-taking application has
some limitations such as the
application should be relatable
with online and offline sources.
5 Big Data in Cloud
Computing: features and
issues [5]
Qualitative analysis
method
Paper written by
Robert Smart
discusses about the
big data and cloud
computing.
The paper discussed
about cloud
computing features
and issues along with
secondary findings.
The paper has some limitations
in providing solutions about
cloud computing issues.

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References
[1]A. Saxena, N. Kaushik, N. Kaushik and A. Dwivedi, "Implementation of Cloud Computing
and Big Data with Java Based Application," InternationalConference on Computing for
Sustainable Global Development (INDIACom), 2016 3rd International Conference on, pp. 1289-
1293, 2016.
[2]H. Singal and S. Kohli, "Amalgamation of Web Analytics with Cloud
Computing," Computing for Sustainable Global Development (INDIACom), 2016 3rd
International Conference on, pp. 2220-2222, 2016.
[3]D. Cvetkovic, M. Mijatovic, M. Mijatovic and B. Medic, "Web service model for distance
learning using cloud computing technologies," In Information and Communication Technology,
Electronics and Microelectronics (MIPRO), 2017 40th International Convention on, pp. 865-
869, May 2017.
[4]P. Neves, B. Schemerl, J. Bernardino and J. Camara, "Big Data in Cloud Computing: features
and issues."
[5]R. Smart, D. Jaramillo, C. Lu and T. Cook, "Implementing a cloud backed scalable note-
taking application with encrypted offline storage and cross platform replication," SoutheastCon
2015, pp. 1-6, April 2015.