An Optimised Method for Fetching and Transforming Survey Data based on SQL and R Programming Language

Abstract

The development of information systems in recent years has contributed to various methods of gathering information to evaluate IS performance. The most common approach used to collect information is called the survey system. This method, however, suffers one major drawback. The decision makers consume considerable time to transform data from survey sheets to analytical programs. As such, this paper proposes a method called 'survey algorithm based on R programming language' or SABR, for data transformation from the survey sheets inside R environments by treating the arrangement of data as a relational format. R and Relational data format provide excellent opportunity to manage and analyse the accumulated data. Moreover, a survey system based on structured query language and R programming language is designed to optimize methods to manage survey systems by applying large features offered via combining multi data science languages. The experiments verified enhancements of flexibility, technical tools, and data visualization features employed to process the collected data from different aspects; therefore, the proposed approach demonstrates a simple case study to enhance the evaluation requirements of the proposed technique. Finally, the estimated results of this research can be used to improve the methods of information management on different aspects such as survey systems and other data models that hold the relational and non-relational models using SABR. This method demonstrated improved accuracy of data collected, reduced data processing time and arranged data to the willing model.

Enhancement of NoSQL Database Performance Using Parallel Processing

“…Despite its schema flexibility, scalability, and agility, NoSQL databases struggle to provide real-time data access and sophisticated analytics (Zhang et al, 2016;Hasan et al, 2019 ). NoSQL databases' parallel processing is promising, suggesting data management's future is optimizing large-scale data processes.…”