Embedding R statistical engine in apache server to visualize electrical energy data in a web-based environment

Teh, Chin Chiat (2014) Embedding R statistical engine in apache server to visualize electrical energy data in a web-based environment. [Final Year Project Report] (Unpublished)

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Abstract

Most of the web-based development projects today are using PHP or other plug-ins to generate graphical data for users to analyze. However, the users may have difficulty to understand these languages because they are technical languages familiar only to those doing web development. Therefore, a cost efficient web application that is using the R statistical language in a web development environment is proposed. The objective of the electrical energy usage visualization web application is to support a wide range of users such as users with statistical background, manager of companies and energy and utility department staff to monitor and analyze their electrical energy usage. This web application visualization process is using the powerful R statistical engine in a web environment to generate graphical output such as scatter plot, bar chart and pie chart using the electrical energy usage data. A customize waterfall methodology has been used in this project. It consists of five stages which are requirement analysis, design, implementation, testing and evaluation and evolution. This web application has four important features which are i) visualize electrical energy usage in different electrical energy parameters, ii) backup dataset, iii) guidelines for electrical energy tariff and iv) user management. For this project implementation, there are two sections which are (i) setup and configuration of the RApache server and follow by the (ii) web application implementation. This web application is using test plan and test case to ensure that all the functions in this web application is fulfill the user requirement. In future, this web application can be enhanced by including the statistical functions such as prediction and customization of R charts to take full advantage of the R statistical engine.

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