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What is your subject about? A brief overview

This subject builds on the extensive review of the research topic in the area of Information Technology (IT) that students commenced in previous coursework subjects in the course. The subject requires students to reflect on their research plan, develop a detailed research proposal, with ethics approval as appropriate, defend their research proposal, and collect data using appropriate data collection techniques. Students will develop expertise relating to a major research issue and will have the opportunity to publish their research in academic conference proceedings or refereed journals.

Task

1. Based on the title, introduction and literature review that you conducted in ITC713 you should now justify the reason for taking an empirical research approach for your paper. You also need to evaluate different research approaches very briefly. Typically, this task should be written within a paragraph of your paper and should be located somewhere in the Methodology or Experiment section depending on your individual project and the paper.  
2. You are expected to develop a new and non-obvious technique/algorithm/solution to answer the problem that you want to address. This is the core contribution of your paper and research. Your proposed solution in ideal case should be rationally and mathematically (if possible) justified i.e. you need to convince your readers why and how the proposed solution advances the knowledge, and is expected to solve the problem. You also need to justify your solution. In other words you need to sell your idea in this section. Ideally, this should be located in the 'Our Technique' or 'Methodology' section of your paper and can be as long as it requires being. Generally this section can be around 30% to 40% of your paper size. So if you are writing a 20 page paper you can afford to spend around 7-8 pages for this. Quality is more important than quantity.  
3. Since this paper is based on an empirical research approach, you need to collect data that is suitable and sensible for your research. In many cases you can download sample data that are generally used for the similar research by the community. You may have some clear idea on the most commonly used datasets from your literature review. In this case you generally need to introduce and explain the datasets in your Experiment section of the paper. It is generally expected to use several datasets (instead of just one) in order to demonstrate the usefulness of the proposed solution/algorithm on more than one datasets to convince your readers that the positive experimental result has not just been achieved by luck. Often researchers use both natural datasets (that can be downloaded from some suitable data repositories as explained above) and synthetic dataset that is created artificially to demonstrate the advantages of the proposed solution to solve the research question. You need to clearly explain the purpose and process of creating the synthetic data so that a reader can recreate the same or very similar datasets if they wanted. However, if you need to collect new data for your research then you may have a new section called 'Data Collection' where you need to explain the purpose and process.

4. Given that you have a novel and convincing algorithm/solution to the research question, and a number of datasets that can be used to demonstrate the strength of your solution you now need to explain your experimental plan i.e. exactly how you are going to use your datasets to demonstrate the strengths of your technique. This can be somewhere at the beginning of your Experiment section in the paper. You need to explain the experiment plan so that a reader understands the significance of the experiment and can repeat the experiments if he/she wanted. In your experiment plan you may want to compare your algorithm/solution with a few existing ones that you have discussed in your literature review section. This will help you to demonstrate the strengths of your technique over the existing ones. In case there is no such existing solution in the literature you may devise one or two sensible and obvious solutions other than the one that you are proposing and show the advantages of your proposed technique. 

The report discusses the exploitation of big data applications deployed in the cloud to understand new sources of value and improved business results. Many companies worldwide are envisioning big data applications and tools to enhance business value. However, making use of big data sources for analytics and processing requires an equally robust IT infrastructure with reliable capacities to effectively handle big data applications. For instance, having a detailed view of market conditions, customer requirements and understanding potential, companies can easily gain value. These aspects are enabled by cloud computing services, an ideal IT service infrastructure to easily handle big data applications. In order to understand the business benefits of such an implementation, the report discusses the empirical research approach to understand whether big data with cloud computing will provide enhanced business value.

Introduction

The area of cloud computing is rapidly evolving due to its advantages in terms of scalability, on-demand service provisioning,  availability and reliability (Hashem, et al. 2015). Cloud systems for its pay-as-you-go pricing model offers significant cost savings compared to deploying an in-house infrastructure (Kar & Rakshit, 2015). Cloud service models offer a variety of benefits for businesses in terms of computing, storage, network, software and infrastructure, and so on. Cloud computing services can be accessed through the internet by using any type of browser software such as Internet Explorer (IE) and Firefox (Sareen, 2013).

Big data is another computing area which offers the potential for business companies to enhance their value (Bughin, Livingston &Marwaha, 2011). Big data includes all types of information to encompass social media feeds, user browsing behaviour, system logs, emails, credit card sales, online transactions, audio/video, and so on, along with internal transactional data. Therefore big data can be understood as, various types of information (structured and unstructured) available on the internet, which can be combined with internal data (Jagdidsh, et al. 2014). However, to implement big data processing solutions internally, the organization would require huge upfront costs (Feinleib, 2015). This is because big data contains large volumes of data sets (in the order of exabytes). Due to cost and infrastructure needs, cloud services are considered as appropriate for big data analysis. Clouds provide capabilities required for processing large datasets (Rajeshwari, 2015). Organizations have adopted big data analytics on the cloud by combining their internal business data to extract information related to business performance and have realized business value (Chang, Kuo, & Ramachandran, 2016). In this report, the topic “Big Data with Cloud Computing: The New Value Enhancer for Companies” is researched. The research approach followed for understanding this topic is empirical research. The report will provide a discussion on research philosophies and the methodology followed in this research. The data collection approach followed, and the experimental plan for conducting this research is covered in the report.

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