i-manager's Journal on Cloud Computing (JCC)


Volume 4 Issue 2 July - December 2017

Article

Cloud Computing Security Threats and Solutions

Stefan Henry* , Md Liakat Ali**
* UG Scholar, Caldwell University, New Jersey, United States.
** Assistant Professor of Computer Science, United States.
Henry, S., Ali, M. L. (2017). Cloud Computing Security Threats and Solutions. i-manager's Journal on Cloud Computing, 4(2), 1-8. https://doi.org/10.26634/jcc.4.2.14249

Abstract

The continued evolution of technology has led to a vast virtualized network of unlimited possibilities; cloud computing. Cloud computing is an internet based service that offers cloud storage, online storage, to clients. With the use of software applications, providers create an end user virtual application that provides the user with software resources along with the necessary hardware components needed to keep all data secured. Clients are given the choice of three service models when purchasing cloud service; Software as a Service, Platform as a Service, and Infrastructure as a Service. In all cloud servers, there are four different deployment models of cloud systems; the public cloud, private cloud, the hybrid cloud and the community cloud. When it comes to the use of cloud systems, even though there are large amount of advantages there are equally a large amount of serious risks that if left unchecked, could lead to serious implications to the user’s data and the provider’s cloud service. Because of the complexity of cloud computing, developing quality security measures is highly challenging. This paper provides a background on what cloud computing is; it dives into details of the service models, the deployment models, what the threats and risks are. This paper also offers notable solutions to combat the increasing threats against cloud systems.

Article

An Introduction to World Leading CRM: SalesForce.com

Harshi Garg* , Uma kumari**
* M.Tech Scholar, College of Engineering and Technology, Mody University of Science and Technology(MUST), Rajasthan, India.
** Assistant Professor, College of Engineering and Technology, Mody University of Science and Technology (MUST), Rajasthan, India
Garg, H., and Kumari, U. (2017). An Introduction to World Leading CRM: SalesForce.com. i-manager's Journal on Cloud Computing, 4(2), 9-14. https://doi.org/10.26634/jcc.4.2.14579

Abstract

In the today’s world where every single person wants to keep their data secure, everyone is switching from their IT setups to cloud. Salesforce.com is the platform which is based on cloud computing which enables us in replacing the existing traditional system where software applications are installed on computer hardware. This paper proposed how the Force.com platform has been changed the traditional system into a world leading CRM. There are numerous reasons which makes IT giants that they should move towards Force.com platform. Salesforce.com is the world leading cloud provider having number of different features one can learn. This paper mainly focuses on the important aspects of Force.com platform. Some of the features are still new to the developer. The proposed work is done to make developer familiar with the editions and versions of this platform. With each release some new features are added to enhance the functionality of the platform.

Research Paper

LiDAR Point Cloud 3D Attribute Extraction and Development of PCL Based Visualization Interface

Teh Peh Chiong* , Lai Koon Chun **, Adrian Teo Wei Hong ***, Ng Kok Leong ****, Ong Chu En *****
* Assistant Professor, Department of Electronic Engineering, Universiti Tunku Abdul Rahman, Malaysia.
** Assistant Professor in the Department of PetroChemical Engineering, Universiti Tunku Abdul Rahman, Malaysia.
*** Engineer, Universiti Tunku Abdul Rahman, Malaysia
****_***** Research Assistant, Universiti Tunku Abdul Rahman, Malaysia.
Chiong, T. P., Leong, N. K., En, O. C., Chun, L. K., and Hong, A. T. W. (2017). LiDAR Point Cloud 3D attribute extraction and development of PCL based visualization interface. i-manager's Journal on Cloud Computing, 4(2), 15-22. https://doi.org/10.26634/jcc.4.2.14250

Abstract

Handling LiDAR data have been growing ever since the high demand in automotive vehicle and surveillance system application. This paper introduces a conversion tool which allows the extraction of point cloud data while rearranging the data to fit Point Cloud Library (PCL) input format which is known as point cloud data (PCD). Scanse Sweep LiDAR is used as an initial guidance in LiDAR data acquisition where it is able to export .csv and .xyz data. Conversion such as cartesian to polar have also been included due to Scanse Sweep hardware .csv format is arranged in polar form. Moreover, converted point cloud is processed using PCL greedy fast triangulation method for 3D modelling and random sample consensus (RANSAC) algorithm used for plane segmentation which each features are combined in a single application constructed using LabVIEW.

Research Paper

Improving the Performance of KNN Classification Algorithms by Using Apache Spark

B. Rajesh* , Asadi Srinivasulu**
* M.Tech Scholar, Department of Software Engineering, Jawaharlal Nehru Technological University Ananthapur, Andhra Pradesh, India.
**Associate Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Andhra Pradesh, India.
Rajesh, B.., and Srinivasulu, A. (2017). Improving the Performance of KNN Classification Algorithms by Using Apache Spark. i-manager's Journal on Cloud Computing, 4(2), 23-32. https://doi.org/10.26634/jcc.4.2.14382

Abstract

Data mining and machine learning are the most interesting research areas which find meaningful information from the large amount of data available, and converts into understandable form for further use. Diabetes is one of the growing diseases all over the world. Health trade professionals desire a reliable prediction system to diagnose polygenic disease. Tools and techniques available will be used to find the appropriate approaches and methods for classification of diabetes and in extracting valuable pattern. The Spark software was employed as a mining tool for diagnosing diabetes. Thus, using the spark, the performance of KNN Classification can be improved.