Pervasive Real-Time Analytical Framework—A Case Study on Car Parking Monitoring

Abstract

Due to the amount of data emerging, it is necessary to use an online analytical processing (OLAP) framework capable of responding to the needs of industries. Processes such as drill-down, roll-up, three-dimensional analysis, and data filtering are fundamental for the perception of information. This article demonstrates the OLAP framework developed as a valuable and effective solution in decision making. To develop an OLAP framework, it was necessary to create the extract, transform and load the (ETL) process, build a data warehouse, and develop the OLAP via cube.js. Finally, it was essential to design a solution that adds more value to the organizations and presents several characteristics to support the entire data analysis process. A backend API (application programming interface) to route the data via MySQL was required, as well as a frontend and a data visualization layer. The OLAP framework was developed for the ioCity project. However, its great advantage is its versatility, which allows any industry to use it in its system. One ETL process, one data warehouse, one OLAP model, six indicators, and one OLAP framework were developed (with one frontend and one API backend). In conclusion, this article demonstrates the importance of a modular, adaptable, and scalable tool in the data analysis process and in supporting decision making.