Automated dynamic reporting system for big data / Anis Soraya Ahmad

Ahmad, Anis Soraya (2019) Automated dynamic reporting system for big data / Anis Soraya Ahmad. Degree thesis, Universiti Teknologi MARA Cawangan Perak Kampus Tapah.


Reporting system is a platform to store the reporting data such as finance report, rainfall distribution data, and many more. Reporting systems help in capturing data that is needed by clerks to provide effective information to the user or organizations. This project consists of developing an Automated Dynamic Reporting System for Big Data. This project is a system for anyone, especially for the clerk to manage the data in numerical form. This project purposed to develop a system that can solve a problem in reporting data in the system such as the clerk returns to the programmer when updates are needed, incurs an ongoing cost for system maintenance and content can get stagnant and out of date. Other than that, inaccurate data reporting can lead to a vastly uniformed decision based on erroneous evidence. The significance of this project development is to prove that a programmer can be eliminated from being a content updater, to reduce clerk dependency on programmer to update data reporting, and to make data reporting more efficient and effective. Other than that, the clerk can update the content without any knowledge of XHTML or requiring FTP transfer of files. A dynamic reporting system calls content from a database and has many advantages in terms of maintenance of content and speed. Thus, the cost incurred by the clerk may reduce. The system developed using Waterfall methodology which contains six phases. The phases consist of; planning, analysis, design, implementation, testing, and documentation.


Item Type: Thesis (Degree)
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Ahmad, Anis Soraya
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > Online data processing
Divisions: Universiti Teknologi MARA, Perak > Tapah Campus > Faculty of Computer and Mathematical Sciences
Keywords: Automated Dynamic; Reporting System; Big Data
Date: January 2019
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