Emerging technologies and Innovation

 

About me: 

­­I’m Nathali Jayasinghe who is currently studying for my Master Degree in Information Technology in Charles Sturt University. For the subject Emerging Technologies and Innovations, I have chosen one of the innovative and fundamental topics in Big Data Analytics Field, which is ‘Statistical Algorithms’. In my study, I would be deeply focusing on Statistical Algorithms which are used in Big Data Analytics. I have selected this topic because all the big data analytics are carried out using different kind of Statistical Algorithms. I always wanted to explore on big data analytics field, Therefore it is essential to have a proper understanding on its fundamentals. This comprehensive study would aid me to achieve my requirement.

 

Project Title: Machine Leaning Algorithms in Spam Detection

 

I will be blogging the weekly updates on my study using the below blog site.

https://thinkspace.csu.edu.au/nathali

 

Project Problem Domain: In the Information Management Field, one of the most critical and important area is ‘how to deal with the massive amount of rapidly increasing of meta data in the current world since everything is becoming digitalized’. In here, it is important to looking to different and each phase which meta data undergo: Techniques using to gather data, the data storing phase, securing the collected data phase and possibly the most critical phase of how the data is interpreted. Applying the most effective data analyzing techniques and algorithms are the keys to extract massive amount of meaningful information from the collected big data. The organizations who are able to do so gain a huge competitive advantage over the other in the industry.

 

Background/Context/Description:

In the current environment, the organizations are focusing on the methods which can derive the collected data into a meaningful and valuable information. This derived information should be valid, accurate, usable and relevant to the organization. Statistical Algorithms comes into picture in this context. Although organizations have been collecting and analyzing their data from a long time ago, having specific algorithms to analyze big data is relatively new. In here, different statistical models are created with effective algorithms to sort the data, classify into different categories, and process to result in valuable information.

 

Project Aim/Objectives:

In my comprehensive study the main objective would be to dive into the different algorithms and techniques which are being used in the big data analytics and to understand the characteristics, similarities and differences, the effectiveness and suitable environments to use the specific algorithm.

 

Scope:

There are different types and methods of Statistical Algorithms which cater to specific environments and requirements. I would be focusing on mainly used and effective algorithms in the field.

 

Deliverables/Outcomes:

In this project the findings and the deliverables would be presented as a paper after the successful completion.

 

Resources:

Bhargavi, P. (2018). Machine Learning Algorithms in Big data Analytics. Research Gate.

 

Bruce, S., Li, Z., Yang, H.-C., & Mukhopadhyay, S. (2019). Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications. IEEE Xplore .